Artificial intelligence for sustainability in fashion and clothing industry: a systematic review of design innovation, production efficiency, consumer engagement, and cultural heritage preservation

1 Introduction

The fashion and clothing industry is widely recognized as one of the most resource-intensive and sustainability-challenged sectors worldwide because of its extensive material consumption, high levels of waste generation, complex global supply chains, and increasing environmental and social impacts. In recent years, sustainability has become a strategic priority across the industry, extending beyond environmental protection to encompass circular economy practices, responsible production and consumption, digital transformation, and the preservation of cultural heritage (Rathore, 2019; Sarker et al., 2022; Nisa et al., 2025; Billah et al., 2026). Consequently, governments, industry stakeholders, and researchers are increasingly seeking innovative technological solutions capable of supporting a more sustainable and resilient fashion ecosystem.

Among these technologies, artificial intelligence (AI) has emerged as one of the most influential drivers of digital transformation within the fashion and clothing industry (Schramm and Mahltig, 2026). Advances in machine learning, deep learning, computer vision, natural language processing, and generative AI have enabled new approaches to decision-making, automation, prediction, and creative design that were previously unattainable (Giri et al., 2019; Singh, 2024; Dhiwar, 2024). As AI technologies continue to mature, their applications have expanded beyond operational efficiency to encompass a broad range of sustainability-related functions throughout the fashion value chain.

Recent studies demonstrate that AI contributes to sustainability through multiple and interconnected pathways. AI-enabled systems have been employed to enhance design creativity through digital prototyping and generative design, improve manufacturing efficiency by optimizing production planning and resource utilization, support more informed consumer decision-making through intelligent recommendation systems, and preserve traditional clothing and textile heritage using computer vision, pattern recognition, and generative technologies (Hu et al., 2024; Ahmad and Abbas, 2026; Yang et al., 2025; Kaur, 2026). Collectively, these developments illustrate that AI is increasingly functioning as a multidimensional enabler of sustainability rather than merely a tool for automation.

Despite this rapid expansion, the existing literature remains fragmented across diverse research domains. Many studies investigate AI applications within individual contexts, such as sustainable design, manufacturing optimization, digital retail, or heritage preservation, while relatively few studies provide an integrated understanding of how these diverse applications collectively contribute to sustainability across the fashion and clothing industry. Furthermore, the literature varies considerably in methodological approaches, technological focus, sustainability dimensions, and levels of empirical validation, making it difficult to establish a comprehensive understanding of current knowledge, identify areas of consensus and disagreement, and determine priorities for future research.

The rapid emergence of generative AI further reinforces the need for a comprehensive synthesis of the literature. Since 2023, advances in large language models, diffusion models, multimodal AI, and AI-assisted design platforms have accelerated the adoption of intelligent technologies throughout the fashion industry, creating new opportunities for sustainable product development, digital consumer engagement, and cultural heritage preservation while simultaneously raising important economic, ethical, governance, and intellectual property considerations (Dwivedi et al., 2023; Ahmed et al., 2025; Nisa et al., 2025). At the same time, increasing attention has been given to Digital Product Passports (DPPs) (Macchion, 2026), AI governance frameworks, transparency, and responsible AI practices as essential components of sustainable digital transformation within the textile and apparel sector. These rapid developments underscore the timeliness of the present review and highlight the need to consolidate the emerging evidence within a single analytical framework.

Accordingly, this study presents a systematic literature review examining how artificial intelligence contributes to sustainability across the fashion and clothing industry. Following the PRISMA 2020 framework, the review synthesizes evidence from 32 studies published between 2019 and 2026 and examines AI applications across four interrelated sustainability domains: design innovation, production efficiency, consumer engagement, and cultural heritage preservation. By integrating evidence across these domains, the review moves beyond isolated technological applications to provide a comprehensive understanding of AI's role in enabling sustainable transformation throughout the fashion value chain.

Unlike previous reviews that primarily examine specific AI technologies or individual sustainability challenges, this review offers an integrated perspective that compares developments across multiple application domains, identifies methodological strengths and limitations, evaluates emerging research trends, and highlights unresolved conceptual and practical challenges. Furthermore, the review proposes a conceptual framework illustrating how AI connects the four sustainability domains within a unified sustainability ecosystem, thereby providing a foundation for future interdisciplinary research, policy development, and industrial practice.

The review is guided by the following research question:

How has artificial intelligence been utilized to advance sustainability in the fashion and clothing industry across design innovation, production efficiency, consumer engagement, and cultural heritage preservation?

By addressing this question, the review aims to provide a comprehensive synthesis of current knowledge, identify emerging research directions, evaluate methodological developments, and highlight future opportunities for advancing the responsible and sustainable application of artificial intelligence within the fashion and clothing industry.

1.1 Research gaps, aims and objectives

Although the application of artificial intelligence (AI) in the fashion and clothing industry has expanded rapidly, the existing literature remains fragmented across different research domains and technological applications. Previous studies have primarily examined AI within specific contexts, such as sustainable manufacturing, circular supply chains, consumer-facing technologies, fashion design, or cultural heritage preservation, with limited efforts to integrate these perspectives into a comprehensive understanding of AI's contribution to sustainability across the fashion value chain (Giri et al., 2019; Sarker et al., 2022; Nisa et al., 2025; Billah et al., 2026). Consequently, there is insufficient synthesis of how AI simultaneously supports design innovation, production efficiency, consumer engagement, and cultural heritage preservation, or how these domains interact to advance sustainable transformation within the fashion and clothing industry.

This gap is particularly important because sustainability in fashion extends beyond environmental performance to include economic resilience, responsible consumption, social value creation, and the preservation of cultural identity. Although recent studies demonstrate the potential of AI to improve design processes, optimize production systems, enhance consumer decision-making, and safeguard traditional clothing heritage, the evidence remains dispersed across disciplines and varies considerably in methodological approaches, technological focus, and levels of empirical validation. As a result, the current state of knowledge lacks an integrated perspective that enables meaningful comparison of research trends, methodological developments, and remaining challenges.

To address these gaps, this study presents a systematic literature review following the PRISMA 2020 framework to synthesize current evidence on AI-enabled sustainability in the fashion and clothing industry. The review seeks to provide a comprehensive synthesis of existing research on AI applications for sustainability while examining their contributions across four interrelated domains: design innovation, production efficiency, consumer engagement, and cultural heritage preservation. In addition, it compares prevailing research trends, methodological approaches, and sustainability outcomes across these domains to identify areas of convergence, divergence, and emerging patterns within the literature. The review further aims to identify existing knowledge gaps, methodological limitations, and future research priorities, and to develop a conceptual framework illustrating how AI integrates these four sustainability domains to support sustainable transformation throughout the fashion and clothing value chain.

Accordingly, the review is guided by the following research question:

How has artificial intelligence been utilized to advance sustainability in the fashion and clothing industry across design innovation, production efficiency, consumer engagement, and cultural heritage preservation?

Based on this central question, the review pursues the following objectives:

• i. To systematically examine how artificial intelligence has been applied to support sustainable design innovation in fashion and clothing industry, including design generation, virtual prototyping, garment customization, and material or sampling reduction.
• ii. To analyze the role of artificial intelligence in improving production efficiency and operational sustainability in fashion and clothing industry, including supply-chain optimization, demand forecasting, waste reduction, circular economy strategies, and resource-efficient manufacturing.
• iii. To investigate how artificial intelligence has been used to influence consumer engagement and sustainable consumption in fashion and clothing industry, including recommendation systems, conversational agents, virtual try-ons, wardrobe management, and responsible purchasing behavior.
• iv. To explore the contribution of artificial intelligence to the preservation, reinterpretation, and transmission of cultural heritage in fashion and clothing industry, including the documentation, classification, generation, and revitalization of traditional costumes, embroidery systems, handloom practices, and heritage dress knowledge.

The remainder of this article is organized as follows. The next section presents the systematic review methodology and outlines the search strategy, eligibility criteria, and study selection process in accordance with PRISMA 2020 guidelines (Page et al., 2021). This is followed by the results section, which presents the descriptive profile of the reviewed studies and synthesizes the findings thematically across the four core domains of the review: design innovation, production efficiency, consumer engagement, and cultural heritage preservation. The discussion then interprets the implications of these findings for sustainable fashion scholarship and practice, identifies current limitations in the literature, and outlines directions for future research. Finally, the article concludes with a summary of the review's contributions and its relevance to the ongoing transformation of sustainability in fashion and clothing industry through artificial intelligence.

2 Methodology

This study employed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) framework to conduct a systematic literature review examining how artificial intelligence contributes to sustainability in the fashion and clothing industry (Page et al., 2021). PRISMA was adopted because it provides a transparent, structured, and replicable approach for identifying, screening, assessing, and synthesizing relevant studies. Database searches were conducted between March 1 and April 30, 2026, using a predefined search strategy to ensure consistency and reproducibility. The review protocol was not pre-registered in PROSPERO, the Open Science Framework (OSF), or any other registry; therefore, no formal pre-registration exists for this review.

A broad range of academic sources was consulted to ensure comprehensive coverage of the literature and to reduce selection bias throughout the review process. The search and identification stages drew on multiple scholarly databases and indexing platforms, including Web of Science, Scopus, ScienceDirect, SpringerLink, MDPI, Wiley Online Library, IEEE Xplore, ACM Digital Library, Taylor & Francis, and Google Scholar. The use of multiple databases was intended to capture interdisciplinary research spanning fashion, textiles, artificial intelligence, sustainability, cultural heritage, and consumer technologies, thereby strengthening the breadth, reliability, and quality of the review corpus.

The search strategy was designed around the central research question and focused on studies located at the intersection of artificial intelligence, sustainability, and the fashion and clothing industry. A combination of keywords and Boolean operators was used to identify relevant studies through title, abstract, and keyword searches. The principal search terms included combinations of “artificial intelligence,” “AI,” “machine learning,” “deep learning,” “computer vision,” “generative AI,” “fashion,” “clothing,” “apparel,” “textiles,” “sustainability,” “circular economy,” “waste reduction,” “consumer engagement,” “cultural heritage,” “traditional costume,” “embroidery,” and “fashion preservation.” Boolean operators such as AND and OR were used to refine and expand the search strings to retrieve the most relevant literature addressing sustainable fashion design, production efficiency, consumer-facing AI systems, and cultural heritage preservation within the fashion and clothing industry.

Study selection, eligibility assessment, quality appraisal, and data extraction were conducted by a single reviewer using predefined inclusion and exclusion criteria and a standardized data extraction form. The use of a single evaluator represents a methodological limitation because independent duplicate screening was not performed, which may increase the potential for selection bias and subjective interpretation during study inclusion and synthesis. To minimize this risk, consistent screening criteria were applied throughout all stages of the review, and each potentially eligible study was reassessed during the full-text screening phase to ensure alignment with the review objectives. Furthermore, the quality appraisal and thematic synthesis followed structured, predefined procedures to enhance consistency, transparency, and reproducibility. Nevertheless, future systematic reviews would benefit from involving two or more independent reviewers to strengthen reliability and reduce the potential for reviewer bias.

Titles and abstracts were screened against the scope of the review, which focused on studies examining AI applications in the fashion and clothing industry with a clear connection to at least one of the following sustainability dimensions: design innovation, production efficiency, consumer engagement, or cultural heritage preservation. Studies focusing on unrelated industrial sectors, non-fashion applications of AI, or sustainability themes without a fashion and clothing context were excluded. Following title and abstract screening, the remaining studies underwent full-text eligibility assessment. During the full-text review, additional studies were excluded if the fashion focus was marginal, the sustainability relevance was insufficient, or the study did not present a substantive AI-related application. Following this multi-stage screening process, 32 studies were retained for the final qualitative synthesis.

2.1 Quality appraisal

To enhance the methodological rigor of the review, the quality of the 32 included studies was systematically appraised using a structured quality assessment framework adapted from the Critical Appraisal Skills Programme (CASP) and the Joanna Briggs Institute (JBI) critical appraisal principles. Because the final sample comprised heterogeneous study designs—including empirical studies, conceptual papers, conference papers, systematic reviews, case studies, bibliometric analyses, and design-oriented research—a single standardized checklist was not appropriate for all studies. Therefore, a unified appraisal framework was developed to assess methodological quality consistently across different research designs. Each study was evaluated against six criteria: (1) clarity of research objectives, (2) appropriateness of the research design, (3) transparency of the AI methodology or analytical procedures, (4) adequacy of the data sources and supporting evidence, (5) clarity and validity of the reported findings, and (6) relevance to the objectives of the present review. Each criterion was assessed using a binary scoring system (1 = criterion satisfied; 0 = criterion not sufficiently addressed), resulting in an overall quality score ranging from 0 to 6. Studies scoring 5–6 were considered high quality, those scoring 3–4 moderate quality, and those scoring 0–2 low quality. The quality appraisal was undertaken to evaluate the strength and reliability of the available evidence and to support interpretation of the findings rather than to exclude studies from the review.

The final sample comprised a diverse body of scholarly work, including journal articles, conference papers, book chapters, and review studies, reflecting the interdisciplinary and rapidly evolving nature of research on artificial intelligence and sustainability in the fashion and clothing industry. The review covered studies published between 2019 and 2026, capturing the rapid evolution of AI applications in fashion, including generative AI, digital fashion platforms, sustainable supply-chain analytics, consumer-facing recommendation systems, digital product passports, and AI-enabled preservation of traditional clothing and textile heritage. The inclusion and exclusion criteria were guided by each study's relevance to the research question, publication type, language, accessibility of the full text, and the extent to which the study addressed artificial intelligence in relation to sustainability in the fashion and clothing industry. Only English-language studies with sufficient methodological or conceptual relevance were included in the final review.

2.2 Data extraction

A standardized data extraction form was developed prior to the synthesis stage to ensure consistency, transparency, and replicability throughout the review process. For each included study, information was systematically extracted regarding the authors, publication year, publication type, country or geographical context, research design, study objectives, AI techniques employed, fashion application area, sustainability dimension addressed, datasets or data sources, principal findings, reported limitations, and recommendations for future research. Additional information concerning the journal or conference source and methodological approach was also recorded to facilitate descriptive analysis and comparison across studies. The extracted information was subsequently verified against the original publications before being entered into the synthesis tables presented in this review.

The complete study selection process is illustrated in the PRISMA 2020 flow diagram (Figure 1), which presents the identification, screening, eligibility, and inclusion stages together with the number of records excluded at each stage. Following data extraction and quality appraisal, the included studies were synthesized using complementary descriptive and thematic analysis. The descriptive analysis summarized publication trends, study characteristics, research methodologies, and the distribution of AI applications across the fashion and clothing industry. Subsequently, a thematic synthesis approach was employed to integrate findings from the heterogeneous body of literature. The extracted findings were reviewed repeatedly to identify recurring concepts, AI application domains, sustainability dimensions, and emerging research patterns. Initial descriptive codes were generated from the extracted data and iteratively grouped into broader analytical categories through constant comparison and refinement. This process resulted in four overarching themes representing the principal contributions of artificial intelligence to sustainability in the fashion and clothing industry: design innovation, production efficiency, consumer engagement, and cultural heritage preservation. These themes formed the analytical framework for the Results and Discussion sections and enabled a systematic comparison of current research trends, methodological developments, research gaps, and future research opportunities.

Table 1 summarizes the search strategy, eligibility criteria, and study selection process used in this systematic review.

Table 1. Inclusion and exclusion criteria of the review.

3 Results and discussion

This section adopts three complementary analytical lenses: (i) descriptive analysis, (ii) content analysis, and (iii) thematic analysis. The descriptive analysis outlines publication trends, study profiles, methodological designs, and areas of contribution across the 32 studies included in the review, illustrating the increasing scholarly attention to the role of artificial intelligence in promoting sustainability in fashion and clothing industry during the period 2019–2026. The content analysis investigates the principal objectives, AI applications, sustainability-related dimensions, and major contributions of the selected studies, with particular emphasis on the use of AI in fashion design, production systems, consumer-oriented applications, and the preservation of cultural heritage. In addition, the thematic analysis synthesizes the findings under the four principal domains that structure this review: design innovation, production efficiency, consumer engagement, and cultural heritage preservation. Collectively, these analytical perspectives provide a comprehensive understanding of how AI is being leveraged to enhance sustainability across the fashion-clothing sector and offer valuable implications for future scholarship, industrial practice, and policy formulation.

3.1 Descriptive analysis

A total of 32 studies were included in this systematic review, representing a diverse body of research on the application of artificial intelligence to sustainability in fashion and clothing industry. These studies span multiple geographical contexts and address a broad range of topics related to sustainable fashion design, production systems, consumer-oriented technologies, and cultural heritage preservation. The main descriptive characteristics of the reviewed studies—including publication year, research methods, thematic focus, principal findings, limitations, and publication sources—are summarized in Table 2.

Table 2. List of analyzed resources in terms of research methods, focus, findings, gaps, and journals.

The literature demonstrates considerable methodological diversity, encompassing review articles, conference papers, empirical case studies, conceptual studies, experimental design-oriented research, and technology-focused investigations. This diversity reflects the interdisciplinary nature of the field, which brings together fashion studies, textile research, sustainability scholarship, cultural heritage studies, and artificial intelligence applications. Across the sample, the literature shows a marked increase in research activity in recent years, particularly in response to the growing use of AI-driven tools in fashion design, supply-chain management, digital consumer engagement, and heritage-related applications.

In terms of research focus, the selected studies reveal that AI is being explored across several interconnected sustainability domains within fashion and clothing industry. A substantial proportion of the literature addresses design innovation and product development, including generative design, digital simulation, customized garments, and AI-supported creative processes. Another major group of studies focuses on production efficiency and sustainable operations, particularly supply-chain optimization, forecasting, circularity, waste reduction, and resource-efficient manufacturing. A third stream of research examines consumer engagement, including recommendation systems, virtual try-ons, wardrobe management, and AI-enabled support for sustainable purchasing decisions. Finally, a growing body of work investigates cultural heritage preservation, including the digital documentation, classification, regeneration, and reinterpretation of traditional costumes, embroidery systems, and regional textile identities. Together, these descriptive findings indicate that the literature on AI and sustainability in fashion and clothing industry is increasingly expanding beyond isolated technical applications toward a broader, system-level understanding of sustainability across the fashion-clothing ecosystem.

3.1.1 Yearly distribution of reviewed research

For this systematic literature review, the selected studies were published between 2019 and 2026. Although artificial intelligence had already begun to appear in fashion-related research before this period, the integration of AI with sustainability in fashion and clothing industry became more visible in recent years as concerns over environmental impact, circularity, responsible consumption, and digital transformation intensified across the sector. In parallel, growing scholarly attention to cultural heritage preservation and the use of AI in traditional costume, embroidery, and heritage-related design further expanded the scope of this research field. As a result, the most substantial body of literature included in this review emerged during the period 2023–2026, reflecting the accelerated adoption of AI-driven tools in sustainable fashion design, production systems, consumer engagement, and cultural heritage applications.

Within the context of this review, the yearly distribution of publications indicates a clear upward trend in research output, particularly in the last few years of the study period. A large proportion of the included studies were published during the 2024–2026 period, which demonstrates the rapid expansion of academic and practical interest in the use of artificial intelligence to support sustainability in fashion and clothing industry. This pattern suggests that AI-driven sustainability in fashion is an emerging and fast-developing field shaped by recent advances in machine learning, generative AI, digital simulation, consumer-facing intelligent systems, and heritage-focused computational applications. The growing number of publications also reflects a broader recognition that sustainability in fashion and clothing industry must be addressed not only through production efficiency and waste reduction, but also through design innovation, consumer behavior, and the preservation of cultural dress heritage. Figure 2 presents the annual distribution of the reviewed studies included in this systematic review.

3.1.2 Types of research methods used

The 32 studies included in this review demonstrate the interdisciplinary and methodologically diverse nature of research on artificial intelligence and sustainability in the fashion and clothing industry. The literature comprises journal articles, review papers, conference proceedings, case studies, and application-oriented research, reflecting both conceptual developments and practical implementations of AI. Review and overview studies primarily synthesize advances in AI for sustainable fashion, manufacturing, consumer engagement, and cultural heritage preservation, while identifying emerging trends and research gaps. In contrast, empirical and case-based studies provide evidence of AI applications in areas such as digital garment design, sustainable textile manufacturing, intelligent supply-chain management, consumer decision-support systems, and the preservation of traditional clothing and textile heritage through computer vision and generative AI. Conference papers further contribute by introducing emerging technologies, including generative AI, conversational agents, lightweight AI models, and digital design tools, thereby capturing recent innovations that have not yet been widely represented in journal publications. Collectively, these diverse methodological approaches provide complementary perspectives on the evolving role of AI in advancing sustainability across the fashion and clothing value chain.

Figure 3 Types of research methods used in the reviewed studies

3.1.3 Key research areas

The reviewed literature shows that research on artificial intelligence for sustainability in fashion and clothing industry is concentrated in four main areas: design innovation, production efficiency, consumer engagement, and cultural heritage preservation. Among these, cultural heritage preservation and production efficiency received the greatest attention, highlighting strong interest in AI applications for preserving traditional clothing and embroidery heritage, as well as for improving sustainable production, circularity, waste reduction, and supply-chain efficiency. Design innovation formed another important research area, focusing on generative design, digital simulation, customized garments, and sustainable product development. By comparison, consumer engagement received relatively less attention, although it remains a growing area of research through recommendation systems, conversational agents, digital wardrobe tools, and AI-supported sustainable purchasing behavior. The reviewed studies demonstrate that AI is being applied across multiple stages of the fashion-clothing ecosystem, from heritage preservation and design processes to production systems and consumer-facing sustainability applications.

Figure 4 Shows Key research areas in the reviewed studies on AI for sustainability in fashion and clothing industry.

3.1.4 Country-wise distribution of papers

The reviewed studies demonstrate a geographically diverse research landscape spanning Asia, Europe, Africa, and North America, highlighting the global relevance of artificial intelligence (AI) to sustainability in the fashion and clothing industry. As shown in Figure 5 substantial share of the reviewed papers originated from Asian contexts, particularly China, India, Bangladesh, Malaysia, Mongolia, and Saudi Arabia, where research has focused strongly on sustainable production systems, traditional clothing preservation, embroidery heritage, and AI-supported design innovation. European contributions were also prominent, with studies from countries such as France, Italy, Greece, Germany, and the United Kingdom examining AI applications in fashion design, sustainability transitions, consumer engagement, and cultural heritage technologies. In addition, Africa contributed through research from Nigeria, particularly in relation to sustainable entrepreneurship and clothing and textile education, while studies linked to the United States and other international contexts addressed sustainable retail, consumer-facing AI tools, and broader digital transformation in fashion systems. Several papers also reflected cross-national or non-country-specific perspectives, especially review studies and conceptual analyses that examined AI and sustainability in fashion at the industry or global level rather than within a single national context. Figure displays the Country-wise distribution of the reviewed studies on AI for sustainability in fashion and clothing industry.

3.2 Content analysis

The content analysis examines the substantive contributions of the 32 reviewed studies by focusing on the ways in which artificial intelligence has been applied to advance sustainability in fashion and clothing industry. While the descriptive analysis identified broad publication patterns, methodological tendencies, and major research areas, the content analysis moves beyond these surface characteristics to evaluate the specific roles, functions, and sustainability contributions of AI across the fashion-clothing ecosystem. Across the reviewed literature, AI was not treated as a single uniform tool; rather, it appeared as a diverse set of computational approaches—including machine learning, deep learning, computer vision, generative AI, diffusion models, recommendation systems, conversational agents, digital simulation, and pattern-recognition tools—applied to different sustainability challenges in fashion and clothing industry.

A close reading of the reviewed studies shows that AI applications in this field can be meaningfully grouped into four interrelated domains. First, AI has been used to support design innovation, particularly through generative design, digital simulation, customized garment development, sustainable product ideation, and heritage-inspired creative processes. Second, AI has played an important role in production efficiency, where it has been linked to supply-chain optimization, waste reduction, circular economy strategies, sustainable manufacturing, forecasting, and climate-resilient apparel systems. Third, a growing set of studies has focused on consumer engagement, including AI-powered recommendation systems, conversational agents, wardrobe tools, customer-experience optimization, and technologies intended to promote more sustainable purchasing and consumption behavior. Finally, a particularly prominent body of literature has examined cultural heritage preservation, demonstrating how AI can assist in the documentation, classification, regeneration, reinterpretation, and safeguarding of traditional costumes, embroidery patterns, and culturally embedded clothing practices.

Accordingly, the content analysis is organized into four subsections that correspond to these major areas of application: design innovation, production efficiency, consumer engagement, and cultural heritage preservation. This structure allows the review to capture both the technological diversity of AI applications and the multidimensional nature of sustainability in fashion and clothing industry. It also provides a basis for comparing where the literature is currently concentrated, where conceptual and empirical gaps remain, and how the reviewed studies collectively position AI as a transformative but unevenly developed force within sustainable fashion systems.

3.2.1 AI for design innovation in sustainable fashion and clothing industry

Artificial intelligence has emerged as a key enabler of sustainable design innovation by supporting digital creativity, product customization, virtual prototyping, and heritage-inspired design. Across the reviewed studies, AI applications extend beyond automation to facilitate data-driven decision-making throughout the design process, enabling designers to improve product development efficiency while reducing material waste and physical sampling. Because design decisions influence resource consumption, product longevity, and consumer acceptance, AI-supported design is increasingly viewed as an important pathway toward sustainability.

Generative AI represents one of the most prominent developments within this domain. Several studies demonstrate its potential to generate innovative fashion concepts while incorporating traditional cultural elements into contemporary designs. For example, Yang et al. (2025), Wang et al. (2024), and Yu et al. (2025) show that generative models can regenerate embroidery motifs and reinterpret traditional textile patterns, thereby supporting both creative innovation and cultural sustainability. Similarly, Wang et al. (2023) demonstrate that AI-supported 3D digital simulation and customization improve garment fit and reduce material waste by minimizing physical prototyping, whereas Kaur (2026) and Ghaith (2025) emphasize the role of AI in optimizing design workflows and integrating eco-innovation into sustainable product development.

Despite these promising developments, the literature reveals important limitations. Most studies remain conceptual, exploratory, or prototype-oriented, with relatively few providing empirical evidence regarding long-term environmental benefits, commercial scalability, or user acceptance. Furthermore, while generative AI is widely promoted as a catalyst for creativity, limited attention has been given to issues such as intellectual property, design originality, and the ethical implications of AI-generated fashion content. Consequently, although AI demonstrates considerable potential to transform sustainable fashion design, further empirical research is needed to evaluate its practical effectiveness and broader sustainability impacts under real-world design and manufacturing conditions.

3.2.2 AI for production efficiency and sustainable fashion systems

Production efficiency represents the most mature and implementation-oriented domain within the reviewed literature, with AI primarily applied to improve resource utilization, optimize manufacturing operations, enhance supply-chain performance, and support circular economy initiatives. Compared with the other sustainability domains, studies in this area provide stronger evidence of AI's operational value, particularly in addressing overproduction, inventory inefficiencies, demand uncertainty, and material waste across the fashion and clothing value chain.

The reviewed studies consistently demonstrate that AI supports sustainable manufacturing by integrating predictive analytics, process optimization, and intelligent decision-making into production systems. Ahmad and Abbas (2026), Kaur (2026), Singh (2024), and Dhiwar (2024) collectively argue that AI enhances production efficiency by reducing waste, optimizing manufacturing processes, and enabling more adaptive and circular production models. Similarly, Sarker et al. (2022), Billah et al. (2026), and Nisa et al. (2025) highlight the growing role of AI in demand forecasting, logistics coordination, inventory management, and supply-chain optimization, demonstrating its contribution to climate resilience, resource efficiency, and value-chain sustainability. Together, these studies indicate a shift from isolated automation toward intelligent production systems that integrate sustainability objectives throughout manufacturing and supply-chain operations.

Despite broad agreement regarding AI's operational benefits, the literature also reveals important methodological limitations. Much of the current evidence is derived from conceptual discussions, review studies, or framework-based analyses rather than large-scale industrial implementation. Consequently, while AI is widely promoted as a mechanism for improving productivity and reducing environmental impacts, relatively few studies provide longitudinal evidence demonstrating measurable improvements in resource consumption, emissions reduction, production costs, or circularity under real manufacturing conditions. Moreover, economic considerations—including implementation costs, workforce reskilling, infrastructure investment, and the accessibility of AI technologies for small and medium-sized enterprises—remain insufficiently explored.

Overall, the reviewed literature positions AI as a strategic enabler of sustainable production systems by combining operational efficiency with environmental objectives. However, future research should move beyond conceptual models toward empirical evaluations that quantify the long-term environmental, economic, and organizational impacts of AI deployment across diverse fashion manufacturing contexts.

3.2.3 AI for consumer engagement and sustainable consumption

Compared with design innovation and production efficiency, the literature on consumer engagement is smaller but increasingly significant, reflecting a growing recognition that sustainability in fashion and clothing industry cannot be achieved through production-side interventions alone. Instead, several reviewed studies emphasize that AI can also influence how consumers discover, evaluate, purchase, experience, and use fashion products, thereby shaping more sustainable consumption patterns. In this domain, AI is typically applied through recommendation systems, conversational agents, digital wardrobe tools, intelligent retail interfaces, and customer-experience optimization systems. These technologies are often designed to support more informed decision-making, increase user interaction with sustainable fashion alternatives, and reduce friction between sustainability goals and consumer behavior.

A key contribution in this area is the work of Hu et al. (2024), who present SHADE, an AI-enabled digital tool intended to empower consumer choice in sustainable fashion. Their study highlights how AI can be used not only to recommend products, but also to structure consumer-facing sustainability information in ways that facilitate more responsible decision-making. Hernandez Manzo et al. (2025) similarly review AI-based conversational agents in sustainable fashion, showing how chatbots and interactive digital assistants can guide consumers toward sustainable products, answer questions, and personalize the shopping experience. These studies indicate that AI can function as a mediator between sustainability information and consumer action, helping users navigate complex choices in a market often characterized by information overload, inconsistent labeling, and limited transparency.

Other studies approach consumer engagement from the perspective of retail experience and purchasing behavior. Bolesnikov et al. (2022) examine perceptions of AI use in optimizing customer purchasing experiences within the sustainable fashion industry, while Burnstine (2025) explores the broader transformation of physical and online fashion retail through AI, including its role in enhancing experiences and supporting sustainability-oriented innovation. Rathore (2019) adds a marketing perspective by discussing the opportunities and challenges of fashion sustainability in the AI era, suggesting that AI-driven personalization, communication, and customer targeting may play a role in how sustainable fashion is positioned and consumed. Jude (2024), although broader in scope, contributes to this discussion by considering how AI may help reshape consumption patterns toward more sustainable behavior.

3.2.4 AI for cultural heritage preservation in fashion and clothing industry

Among the four sustainability domains, cultural heritage preservation represents one of the most distinctive applications of AI, extending sustainability beyond environmental and operational objectives to include cultural continuity, identity preservation, and the safeguarding of traditional knowledge. Across the reviewed studies, AI is employed to document, classify, regenerate, and reinterpret traditional garments, embroidery, textile patterns, and regional dress traditions, demonstrating its growing role in preserving both tangible and intangible fashion heritage.

The reviewed literature identifies two complementary approaches to AI-enabled heritage preservation. The first focuses on documentation and classification through computer vision and deep learning. Wang et al. (2024), Khan and Zhang (2026), Baalamash et al. (2024), Halabi et al. (2025), and Khayankhyarvaa et al. (2025) demonstrate how AI can classify traditional costumes, recognize embroidery patterns, and digitally archive culturally significant garments across diverse heritage contexts, including Saudi Arabia, Malaysia, Mongolia, and South Asia. These applications improve the documentation, accessibility, and long-term preservation of cultural assets that may otherwise be vulnerable to loss or fragmentation.

The second approach emphasizes regeneration and creative reinterpretation of heritage fashion. Deng et al. (2023), Yang et al. (2025), Yu et al. (2025), Paliwal and Kaur (2025), and Liu et al. (2025) illustrate how generative AI, diffusion models, and digital archives can revitalize traditional motifs and support heritage-inspired contemporary fashion. Rather than functioning solely as preservation tools, these technologies enable the adaptation of traditional design knowledge to modern contexts, thereby strengthening cultural sustainability through continued visibility and creative reuse.

Despite these advances, the literature also reveals important challenges. Most studies evaluate technical feasibility or specific cultural case studies, with limited evidence regarding long-term societal impact, community acceptance, or the effectiveness of AI in preserving intangible cultural knowledge. Moreover, ethical issues—including cultural authenticity, ownership of traditional knowledge, intellectual property rights, and the potential commercialization of culturally significant designs—remain insufficiently examined. Consequently, while AI demonstrates considerable potential to safeguard and revitalize fashion heritage, future research should adopt more interdisciplinary and community-centered approaches that evaluate not only technological performance but also cultural, ethical, and social outcomes.

3.2.5 Critical synthesis and conceptual integration of AI for sustainable fashion

The reviewed literature demonstrates that artificial intelligence contributes to sustainability across multiple dimensions of the fashion and clothing industry; however, the maturity, evidence base, and practical implementation of these applications vary considerably across the four thematic domains. Design innovation and cultural heritage preservation are characterized by rapid technological advances, particularly in generative AI, computer vision, and digital design tools, yet much of this literature remains exploratory and focuses primarily on proof-of-concept systems, prototype development, or conceptual design frameworks. In contrast, studies addressing production efficiency provide stronger evidence of practical implementation, particularly in relation to supply-chain optimization, demand forecasting, waste reduction, and circular production systems. Consumer engagement occupies an intermediate position, with growing evidence supporting AI-enabled recommendation systems, conversational agents, and personalized sustainability guidance, although relatively few studies demonstrate sustained behavioral change or measurable reductions in unsustainable consumption.

A comparative analysis also reveals important differences in how sustainability is conceptualized across the literature. Studies concerned with production efficiency predominantly emphasize environmental and operational sustainability through resource optimization, waste minimization, and manufacturing efficiency. In contrast, heritage-oriented studies broaden the sustainability discourse by emphasizing cultural continuity, preservation of traditional knowledge, and protection of regional identities. Design-focused studies frequently combine environmental and cultural sustainability through digital prototyping, customized product development, and heritage-inspired creative practices, whereas consumer-oriented research highlights social sustainability by promoting informed purchasing decisions and greater consumer participation in sustainable fashion ecosystems. These differences illustrate that AI contributes to sustainability through complementary rather than isolated pathways, suggesting that sustainable transformation within the fashion industry requires simultaneous progress across technological, environmental, economic, social, and cultural dimensions.

The review further identifies several contradictions within the existing literature. While numerous studies present AI as a powerful enabler of sustainability, relatively few provide robust empirical evidence demonstrating long-term environmental, economic, or social benefits under real industrial conditions. Many investigations infer sustainability improvements from increased efficiency, digitalization, or automation without directly measuring reductions in resource consumption, carbon emissions, or waste generation. Similarly, although generative AI is widely promoted as a catalyst for creativity and heritage revitalization, concerns remain regarding cultural authenticity, ownership of traditional knowledge, intellectual property rights, and the potential homogenization of culturally distinctive fashion traditions. These findings indicate that technological capability does not necessarily guarantee sustainable or culturally responsible outcomes, highlighting the importance of governance, ethical oversight, and context-sensitive implementation.

Economic considerations also emerge as an important yet comparatively underexplored dimension of AI adoption. Although AI offers opportunities to improve productivity, optimize supply chains, reduce production costs, and support more efficient resource allocation, successful implementation often requires substantial investments in digital infrastructure, high-quality datasets, computational resources, and workforce training. These requirements may create barriers for small and medium-sized fashion enterprises, independent designers, and artisan communities that possess significant cultural heritage but limited technological capacity. Consequently, unequal access to AI technologies may inadvertently widen digital and economic disparities within the fashion sector unless accompanied by appropriate policy support and capacity-building initiatives.

Ethical considerations similarly require greater attention in future research. Across the reviewed studies, relatively limited discussion is devoted to algorithmic transparency, explainability, data governance, cultural ownership, informed consent for heritage digitization, and the responsible use of generative AI. Heritage-related applications are particularly sensitive because AI systems trained on culturally significant garments, embroidery patterns, and traditional motifs may reproduce, modify, or commercialize cultural expressions without adequate recognition of their historical, social, or community significance. These issues underscore the need for ethical governance frameworks that balance technological innovation with cultural integrity, intellectual property protection, and respect for traditional knowledge systems.

Based on the synthesis of the reviewed studies, a conceptual framework is proposed in which artificial intelligence functions as an enabling technology that connects four interdependent sustainability domains: design innovation, production efficiency, consumer engagement, and cultural heritage preservation. Rather than operating independently, these domains reinforce one another throughout the fashion value chain. AI-enabled design innovation facilitates sustainable product development and heritage-inspired creativity; production intelligence enhances operational efficiency and circular manufacturing; consumer-oriented AI encourages informed purchasing and sustainable consumption; and heritage-preservation technologies safeguard cultural identity while enabling contemporary reinterpretation of traditional fashion. Together, these interconnected domains demonstrate that the contribution of AI to sustainable fashion extends beyond technological automation to encompass environmental stewardship, economic resilience, social responsibility, and cultural sustainability. The conceptual framework presented in Figure 6 provides a holistic representation of these interrelationships and illustrates how AI serves as the central enabler linking the four sustainability domains within the fashion and clothing industry.

To provide a concise synthesis of the findings, Table 3 summarizes how artificial intelligence has been applied across the four sustainability domains identified in this review. The table directly addresses the review question by outlining the principal AI technologies, their major applications, the sustainability contributions reported in the literature, and representative studies supporting each domain.

Table 3. Summary of AI applications across the four sustainability domains in the fashion and clothing industry.

3.3 Thematic analysis

The thematic analysis synthesizes the 32 included studies to identify the dominant ways in which artificial intelligence has been mobilized to advance sustainability in fashion and clothing industry. Across the reviewed literature, AI does not appear as a single uniform intervention; rather, it is applied through multiple computational approaches—including machine learning, deep learning, generative AI, diffusion models, recommendation systems, conversational agents, computer vision, and digital simulation—to address different sustainability challenges across the fashion-clothing value chain. When the findings of the reviewed studies are examined collectively, four overarching themes become apparent: (1) AI as a catalyst for sustainable design innovation; (2) AI as an enabler of production efficiency, circularity, and supply-chain sustainability; (3) AI as a mediator of consumer engagement and sustainable consumption; and (4) AI as a mechanism for cultural heritage preservation and revitalization. These themes are analytically distinct, yet they remain interconnected in practice, reflecting the multidimensional nature of sustainability in fashion and clothing industry.

3.3.1 AI as a catalyst for sustainable design innovation

One of the most visible themes in the reviewed literature is the role of AI in reconfiguring fashion design processes toward more sustainable, adaptive, and culturally informed modes of innovation. In this theme, AI is positioned not simply as a digital tool for accelerating creative work, but as a mechanism for reducing waste during product development, improving customization, enabling virtual experimentation, and generating new design pathways rooted in sustainability principles. This line of research suggests that the design stage is increasingly understood as a critical intervention point in sustainable fashion, and that AI can influence this stage by altering how garments are conceived, tested, personalized, and aesthetically developed (see Figure 7).

Several studies explicitly demonstrate the value of generative and AI-assisted design systems for sustainable fashion creation. Yang et al. (2025) show that generative AI can support sustainable fashion innovation by revitalizing traditional culture through modern design crafts, thereby linking design creativity with both cultural continuity and sustainable product development. Likewise, Wang et al. (2024) explore digital generative art as a means of preserving and reinterpreting traditional costume embroidery patterns, while Yu et al. (2025) use diffusion models to generate Miao embroidery motifs for sustainable design applications grounded in intangible cultural heritage. These studies indicate that AI can function as a creative co-design mechanism, helping designers reinterpret traditional visual resources while avoiding purely repetitive or extractive design processes.

AI also appears in the literature as a tool for design optimization, customization, and digital prototyping. Wang et al. (2023) demonstrate how 3D digital simulation combined with machine-learning-supported human–product interaction can support the design of customized garments, thereby reducing trial-and-error production and potentially minimizing material waste. Kaur (2026) similarly emphasizes AI's role in optimizing design processes and reducing supply-chain waste, suggesting that design-stage intelligence has downstream sustainability implications for production and inventory management. Ghaith (2025) extends this perspective by linking AI with eco-innovation and sustainable product development, while Deng et al. (2023) show how machine learning can be used in ethnic clothing design to merge innovation with heritage-based aesthetics. Collectively, these studies reveal that AI-supported design innovation is not limited to visual novelty; it is increasingly framed as a strategy for resource efficiency, personalization, reduced physical sampling, and culturally responsive product development.

3.3.2 AI as an enabler of production efficiency, circularity, and supply-chain sustainability

A second major theme concerns the use of AI to improve the operational sustainability of fashion and textile production systems. Within this theme, AI is primarily framed as an enabling technology for waste reduction, process optimization, circular economy implementation, supply-chain coordination, forecasting, and climate resilience. The reviewed studies consistently suggest that sustainability in fashion cannot be addressed solely through aesthetic or consumer-level interventions; it also requires systemic improvements in how apparel is manufactured, distributed, and managed across increasingly complex production networks (see Figure 8).

A number of studies directly connect AI with circular and sustainable production systems. Ahmad and Abbas (2026) position AI within sustainable textile manufacturing and circular economy models aimed at waste reduction and cultural preservation. Singh (2024) provides a broader industry overview, highlighting the role of AI in reshaping fashion and apparel operations, while Dhiwar (2024) similarly argues that AI and machine learning are transforming design, production, and sustainability practices in fashion. Kaur (2026) specifically emphasizes the capacity of AI to optimize processes and reduce waste in fashion supply chains, reinforcing the idea that sustainability gains can be achieved through data-driven process management rather than through end-of-pipe corrections alone.

This theme is further strengthened by studies focusing on apparel supply chains and system-level sustainability frameworks. Sarker et al. (2022) propose an integrative framework for AI in sustainable and climate-resilient apparel supply chains, highlighting functions such as forecasting, decision support, and resilience planning. Nisa et al. (2025) frame AI as a driver of circular economy transformation in fashion and textiles, while Billah et al. (2026) review the adoption of AI technologies for sustainability in the ready-made garment sector. Together, these studies suggest that AI is increasingly being conceptualized as part of a broader digital sustainability infrastructure in fashion—one that can support efficiency, adaptability, and resource conservation at multiple stages of production. At the same time, the literature also reveals a persistent gap between conceptual promise and implementation evidence, as many studies discuss AI's potential in strategic terms without fully measuring long-term environmental outcomes in real industrial settings.

3.3.3 AI as a mediator of consumer engagement and sustainable consumption

A third theme emerging from the reviewed studies is the role of AI in shaping consumer-facing sustainability practices. Compared with design innovation and production efficiency, this area is somewhat smaller in volume, but it is conceptually important because it shifts attention from supply-side interventions to the demand side of fashion sustainability. In this theme, AI is used to influence how consumers discover products, interpret sustainability information, make purchasing decisions, and engage with digital retail environments. Rather than focusing directly on manufacturing efficiency, these studies examine AI as a tool for behavioral guidance, personalization, decision support, and the promotion of more responsible consumption choices (see Figure 9).

AI can support sustainable consumption by improving consumer decision-making through intelligent recommendation systems, conversational agents, and personalized retail platforms. Hu et al. (2024) and Hernandez Manzo et al. (2025) demonstrate that AI-enabled decision-support tools and conversational agents help consumers navigate sustainability information, identify environmentally responsible products, and personalize their shopping experiences. Similarly, Bolesnikov et al. (2022) and Burnstine (2025) highlight the role of AI in enhancing customer engagement and retail experiences through personalized recommendations and intelligent digital interfaces. From a broader perspective, Rathore (2019) and Jude (2024) argue that AI-driven personalization and digital communication have the potential to influence sustainable consumption patterns by improving access to sustainability information and encouraging more informed purchasing decisions.

Despite these promising developments, consumer-oriented applications remain comparatively less mature than those targeting design innovation and production efficiency. Most studies focus on conceptual models, user perceptions, or prototype systems rather than providing empirical evidence that AI-driven interventions lead to sustained behavioral change, reduced overconsumption, or greater adoption of circular consumption practices. Furthermore, limited attention has been given to ethical considerations such as consumer privacy, algorithmic transparency, and the potential for AI-driven personalization to stimulate increased consumption rather than promote sustainability. These findings highlight the need for more rigorous empirical research examining the long-term effectiveness and responsible implementation of AI-enabled consumer applications.

3.3.4 AI as a mechanism for cultural heritage preservation and revitalization

The fourth theme highlights the growing role of AI in advancing the cultural dimension of sustainability within the fashion and clothing industry. Unlike the preceding themes, which primarily emphasize environmental and operational improvements, this body of literature positions AI as a means of preserving cultural identity, safeguarding traditional knowledge, and ensuring the continuity of heritage fashion. Recent bibliometric studies further demonstrate that AI-driven research in textile and fashion design has expanded rapidly, with cultural heritage preservation and sustainable design emerging as important and increasingly interconnected research directions (Bulut and Buzol Mulayim, 2026; Buzol Mulayim and Bulut, 2026a,b). Across the reviewed studies, AI supports both the documentation of cultural assets and the creative reinterpretation of traditional garments, demonstrating that sustainability encompasses not only environmental stewardship but also the protection and revitalization of cultural heritage.

The literature reveals two complementary approaches. The first employs AI for the documentation, classification, and digital archiving of traditional costumes, embroidery, and regional dress through computer vision and deep learning techniques (Baalamash et al., 2024; Halabi et al., 2025; Khan and Zhang, 2026; Khayankhyarvaa et al., 2025; Wang et al., 2024). These studies emphasize the role of AI in improving the accessibility, preservation, and long-term safeguarding of culturally significant fashion assets. The second approach extends beyond preservation by using generative AI and digital design technologies to reinterpret traditional motifs and integrate heritage elements into contemporary fashion (Deng et al., 2023; Liu et al., 2025; Paliwal and Kaur, 2025; Stergiou and Vosinakis, 2025; Yang et al., 2025; Yu et al., 2025). Collectively, these studies suggest that AI can facilitate cultural continuity by enabling heritage knowledge to evolve rather than remain confined to archival preservation.

A critical comparison of the literature, however, reveals that most heritage-oriented studies emphasize technological feasibility while giving comparatively limited attention to broader cultural outcomes. This observation is consistent with recent bibliometric evidence indicating that the field has experienced substantial growth but remains concentrated on technological innovation, while ethical, cultural, and governance dimensions receive comparatively less scholarly attention (Bulut and Buzol Mulayim, 2026; Buzol Mulayim and Bulut, 2026a). Although AI demonstrates considerable potential to preserve and revitalize traditional fashion, relatively few studies evaluate community engagement, long-term cultural impact, or the perspectives of heritage practitioners. Furthermore, ethical issues—including cultural authenticity, ownership of traditional knowledge, intellectual property rights, and the risk of commodifying culturally significant designs through generative AI—remain insufficiently addressed. These findings indicate that the successful application of AI to heritage fashion requires not only technological innovation but also governance frameworks that ensure culturally responsible and ethically grounded implementation.

The reviewed evidence suggests that cultural heritage preservation is no longer a peripheral aspect of sustainable fashion research but a core sustainability domain in which AI supports the protection, transmission, and contemporary revitalization of traditional dress cultures. This conclusion is also reflected in recent bibliometric analyses, which identify cultural sustainability, AI-enabled design, and smart textiles as emerging and influential research themes within the broader AI–fashion literature (Bulut and Buzol Mulayim, 2026; Buzol Mulayim and Bulut, 2026a,b). This theme broadens the understanding of sustainability by demonstrating that AI can simultaneously contribute to environmental, social, and cultural resilience, particularly in regions where clothing serves as an important expression of historical identity and collective memory.

3.3.5 Integrative interpretation of the thematic findings

Taken together, the four themes reveal that the relationship between AI and sustainability in fashion and clothing industry is structurally multidimensional. AI is being used not only to optimize production systems, but also to redesign garments, influence consumer decision-making, and preserve culturally embedded forms of dress knowledge. The reviewed studies therefore position AI as a cross-cutting sustainability infrastructure that operates across environmental, operational, behavioral, and cultural dimensions of the fashion system. At the same time, the thematic synthesis also highlights an uneven research landscape. Studies on production efficiency and cultural heritage preservation are particularly prominent, whereas research on consumer engagement remains comparatively limited and design innovation is often discussed through exploratory or prototype-oriented cases rather than long-term implementation evidence.

The thematic analysis also suggests that many studies still treat these domains in relative isolation. For example, research on cultural heritage preservation often does not connect explicitly with supply-chain sustainability or consumer behavior, while consumer-facing AI tools are rarely linked to heritage value or circular production systems. As a result, one of the clearest implications of the review is the need for more integrative research models that examine how AI can simultaneously support sustainable design, responsible production, consumer engagement, and cultural continuity. Such an approach would better reflect the complexity of sustainability in fashion and clothing industry and would move the field beyond fragmented applications of AI toward a more holistic understanding of how digital intelligence can support long-term transformation in fashion systems.

4 Conclusion and research implications

This systematic review examined how artificial intelligence has been utilized to advance sustainability in fashion and clothing industry across four interrelated domains: design innovation, production efficiency, consumer engagement, and cultural heritage preservation. Drawing on 32 studies published between 2019 and 2026, the review demonstrates that AI has emerged as a versatile and increasingly influential force in the transformation of fashion sustainability. Rather than operating as a single-purpose technological intervention, AI appears in the reviewed literature as a broad ecosystem of tools—including machine learning, deep learning, generative AI, diffusion models, computer vision, recommendation systems, conversational agents, and digital simulation technologies—that are being applied to different sustainability challenges across the fashion-clothing lifecycle.

The review findings show that AI is contributing to sustainability in fashion and clothing industry through several distinct yet interconnected pathways. In the area of design innovation, AI supports generative design, digital prototyping, customized garment development, and the reinterpretation of heritage-inspired aesthetics, thereby reducing dependence on repetitive physical sampling and opening new possibilities for more adaptive, resource-conscious design processes. In relation to production efficiency, AI has been linked to sustainable textile manufacturing, circular economy models, supply-chain optimization, forecasting, waste reduction, and climate-resilient apparel systems, indicating its growing role in improving operational sustainability within fashion production networks. The literature also reveals an expanding, though still comparatively limited, body of work on consumer engagement, where AI is used to enhance recommendation systems, conversational retail tools, decision-support platforms, and digital consumer interfaces that may encourage more sustainable purchasing behavior. Finally, cultural heritage preservation emerged as one of the most distinctive themes in the review, with numerous studies demonstrating how AI can document, classify, regenerate, reinterpret, and revitalize traditional costumes, embroidery systems, handloom traditions, and other culturally embedded clothing practices.

Taken together, the findings suggest that AI is reshaping sustainable fashion not only at the level of industrial efficiency, but also at the levels of creative practice, cultural continuity, and consumer interaction. This is a significant contribution to the sustainability literature, because it shows that AI in fashion and clothing industry should not be understood solely as a tool for automation or productivity enhancement. Instead, the reviewed studies position AI as a multi-dimensional sustainability infrastructure capable of supporting environmental objectives, operational optimization, digital consumer engagement, and the safeguarding of fashion-related cultural heritage. At the same time, the review also reveals that the field remains unevenly developed. Research is currently concentrated most heavily in production-oriented sustainability and cultural heritage preservation, while fewer studies have examined long-term consumer behavior change, comparative implementation outcomes, or the integration of multiple sustainability dimensions within a single AI-enabled fashion system.

4.1 Theoretical contribution

From a theoretical perspective, this review advances the emerging scholarship on AI-enabled sustainability in the fashion and clothing industry by providing an integrated conceptual synthesis that extends beyond previous reviews, which have generally examined AI applications within isolated domains such as manufacturing, supply-chain management, consumer technologies, or cultural heritage preservation. In contrast, this review conceptualizes AI as a multidimensional sustainability infrastructure that simultaneously connects design innovation, production efficiency, consumer engagement, and cultural heritage preservation within a unified analytical framework. The proposed conceptual framework demonstrates that these four domains are not independent areas of application but mutually reinforcing dimensions of sustainable fashion transformation. By integrating environmental, economic, social, and cultural perspectives, this review offers a broader theoretical understanding of how AI reshapes sustainability across the fashion value chain and provides a foundation for future interdisciplinary research on digitally enabled sustainable fashion ecosystems.

4.2 Practical implications

The findings also have important practical implications for multiple stakeholder groups. Fashion designers can leverage generative AI, digital prototyping, and intelligent design systems to reduce material waste, improve product customization, and incorporate cultural heritage into contemporary fashion collections. Manufacturers and supply-chain managers can employ AI-driven forecasting, production optimization, and circular manufacturing systems to improve operational efficiency, reduce waste, and strengthen supply-chain resilience. Retailers and digital commerce platforms can utilize recommendation systems, conversational agents, and decision-support technologies to enhance consumer engagement while promoting more sustainable purchasing behaviors. Museums, heritage institutions, and artisan communities can adopt computer vision, digital archiving, and generative AI technologies to document, preserve, and revitalize traditional clothing, embroidery, and textile heritage, thereby supporting cultural sustainability and intergenerational knowledge transfer. Educators may integrate AI-enabled digital tools into fashion and textile curricula to prepare future professionals with competencies in sustainable digital design and responsible AI applications. Finally, policy makers and industry regulators can use the findings to inform governance frameworks that encourage responsible AI adoption while addressing issues of transparency, intellectual property, cultural ownership, algorithmic fairness, and ethical data governance.

4.3 Future research

Despite the rapid growth of AI applications in sustainable fashion, several important research gaps remain. Future studies should prioritize longitudinal and implementation-based investigations that evaluate the long-term environmental, economic, social, and cultural impacts of AI adoption under real industrial conditions. Greater attention should also be given to comparative studies examining different AI technologies across multiple sustainability domains, as well as interdisciplinary research integrating engineering, fashion design, sustainability science, and cultural heritage studies. Furthermore, future research should develop robust governance frameworks that address ethical issues associated with generative AI, including cultural authenticity, intellectual property protection, algorithmic transparency, and responsible use of heritage data. Addressing these challenges will be essential to ensuring that AI contributes not only to technological innovation but also to equitable, culturally respectful, and sustainable transformation within the global fashion and clothing industry.

4.4 Limitations

This review has several limitations that should be considered when interpreting its findings. First, although the review followed a systematic PRISMA 2020 methodology, the final synthesis was based on 32 studies, representing the current state of a rapidly evolving research field. Given the accelerating pace of developments in artificial intelligence—particularly generative AI—new evidence may emerge quickly and alter existing research trends and conclusions.

Second, the review was restricted to English-language publications. Consequently, relevant studies published in other languages may have been excluded, introducing potential language bias. This limitation is particularly important in the context of cultural heritage and traditional fashion, where significant research may be published in local languages in regions with rich textile and dress traditions.

Third, the review primarily considered peer-reviewed academic publications and selected conference proceedings. Gray literature—including industry reports, government publications, technical reports, white papers, and organizational documents—was largely excluded because these sources often lack consistent peer-review standards. Although this approach strengthened the scientific rigor of the review, it may have limited the inclusion of recent industrial innovations, policy initiatives, and practical applications of AI that have not yet been reported in the academic literature.

Fourth, the existing evidence base remains dominated by conceptual papers, review articles, conference papers, and prototype-oriented studies, whereas comparatively fewer investigations provide robust empirical validation or large-scale industrial implementation. As a result, many reported sustainability benefits are based on proposed frameworks, pilot applications, or proof-of-concept systems rather than longitudinal evaluations conducted in real-world fashion production, retail, or heritage preservation settings. Consequently, the review reflects the current direction and potential of AI-enabled sustainability more strongly than its demonstrated long-term impacts.

Despite these limitations, the review provides a comprehensive and methodologically rigorous synthesis of the current literature. By integrating evidence across design innovation, production efficiency, consumer engagement, and cultural heritage preservation, it offers a valuable foundation for future empirical research, interdisciplinary collaboration, and the responsible adoption of AI in sustainable fashion and clothing systems.

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Footnotes

Edited by: Konstantinos Salonitis, Cranfield University, United Kingdom

Reviewed by: Selma Bulut, Kirklareli University, Türkiye

Ioana-Crina Pop-Cohut, University of Oradea, Romania