AI-driven smart home optimization for sustainable energy and water management: a systematic review

The increasing intricacy of smart home systems, propelled by the incorporation of renewable energy sources, IoT technologies, and water-dependent subsystems, demands sophisticated frameworks for effective and sustainable resource management. This paper offers a thorough evaluation of AI-based strategies for optimizing energy and water usage in smart homes, with the objective of assessing current methodologies and pinpointing research deficiencies. The review adheres to PRISMA rules and examines papers published from 2021 to 2025, obtained from Google Scholar and IEEE Xplore, with supplementary inclusion of works indexed in Scopus and Web of Science according to specified eligibility criteria. A theme synthesis methodology is utilized to analyze five principal domains: smart home energy management, non-intrusive load monitoring, reinforcement learning-based control, smart water management, and IoT security. The results demonstrate that although AI methodologies like machine learning, deep learning, and reinforcement learning markedly enhance energy efficiency, cost optimization, and real-time decision-making, the majority of research concentrates on single-resource systems and lacks comprehensive energy-water optimization. Further deficiencies encompass restricted practical implementation, inadequate economic assessment, and ongoing security issues. This review provides a thorough synthesis of recent achievements and emphasizes the necessity for integrated, scalable, and context-aware frameworks to facilitate sustainable smart home growth.

The swift progression of digital technology has profoundly transformed domestic resource management, fostering worldwide interest in smart houses as essential facilitators of sustainable living. Smart home systems amalgamate Internet of Things (IoT) devices, artificial intelligence (AI), sophisticated sensor technologies, wireless communication protocols, and intelligent control algorithms to automate and enhance domestic processes. These technologies are progressively utilized to tackle urgent issues concerning energy consumption, water scarcity, escalating utility expenses, environmental sustainability, and user convenience. In areas like Nigeria, where households often face inconsistent electricity supplies, ineffective water delivery, and escalating operational expenses, smart home optimization offers a timely and transformative solution. Studies across various fields indicate that data-driven automation can markedly enhance resource efficiency, diminish operational waste, increase convenience, and facilitate overarching sustainability objectives (e.g., energy-related research such as (Houshangi, 2025) and (Babu et al., 2025); water-related research such as (Sathyamoorthy and Sri, 2025)).

The proliferation of smart house research has generated a varied ecosystem of products and approaches aimed at enhancing domestic efficiency. Home energy management systems (HEMS) employ sensing infrastructure, consumption modeling, and optimization frameworks to synchronize appliance operation, mitigate peak demand, lower electricity expenses, and enhance grid interaction. AI techniques applied in smart home optimization extend beyond reinforcement learning and include supervised learning models (e.g., regression, decision trees), deep learning approaches such as LSTM networks, clustering methods, hybrid ensemble models, and predictive analytics frameworks (Babu et al., 2025). Methods include machine learning, fuzzy logic control, hybrid predictive systems, and reinforcement learning have demonstrated enhancements in real-time decision-making and facilitated adaptive scheduling techniques (Aldahmashi and Ma, 2024), (Babu et al., 2025), and (TLDR, 2025)). Non-intrusive load monitoring (NILM) has developed as a supplementary method for disaggregating household energy data without the need for multiple meters, facilitating detailed insights into appliance-level activity and aiding intelligent energy saving measures.

In conjunction with energy-centric advancements, smart water management technologies have gained traction due to escalating water scarcity, deteriorating infrastructure, and pollution in numerous areas. IoT-enabled water systems provide real-time monitoring of flow, pressure, tank levels, water quality, and leakage incidents, facilitating prompt interventions and enhancing supply reliability. Advanced systems utilize digital twins, anomaly detection, and predictive analytics to model distribution networks, optimize treatment procedures, and improve resilience in uncertain environmental conditions as illustrated in (Kotwal, 2025), (Syed et al., 2024), and (Al-Rashidi et al., 2025)). These methodologies are especially pertinent for evolving environments where water losses, inefficiency, and sporadic supply continue to be significant issues.

As smart home technologies become increasingly integrated and interconnected, concerns concerning security, privacy, interoperability, scalability, and user trust also escalate (Wavale and Sireesha, 2025), (Sikdar et al., 2024). IoT ecosystems frequently depend on diverse devices with differing security levels, rendering them susceptible to cyberattacks, unauthorized access, data manipulation, and privacy violations. Research regularly identifies these vulnerabilities as significant obstacles to widespread adoption, particularly in resource-limited environments, as evidenced in (Ganti et al., 2025). Moreover, several current smart home systems are primarily concentrated on optimizing individual resources, neglecting the intricate interrelations among energy, water, user comfort, cost, and environmental limitations.

A thorough review is necessary to combine evidence from the developing literature and technical landscape, identify advancements and constraints, and create a coherent basis for future smart home advances. This review analyzes literature published from 2021 to 2025 across five interrelated thematic domains: smart home energy, non-intrusive load monitoring, reinforcement learning for home energy management systems (HEMS), smart water management, and IoT privacy/security, to furnish a thorough understanding of cutting-edge advancements. The insights obtained will guide the development of a dual-resource intelligent optimization framework relevant for developing scalable and context-aware smart home optimization of improving sustainable energy and water management in Nigerian households. The main aim of this work, therefore is to thoroughly review and synthesize recent advancements in AI-driven smart home technology for integrated energy and water management. This study specifically aims to: (i) analyze contemporary methodologies in smart home energy management, non-intrusive load monitoring, reinforcement learning-based control, smart water management, and IoT security; (ii) assess the efficacy and constraints of current techniques in attaining multi-resource optimization; and (iii) pinpoint critical research deficiencies and prospects for advancing scalable, adaptive, and secure smart home systems, particularly in resource-limited contexts such as Nigeria. Recent advances further extend beyond traditional machine learning and reinforcement learning to include deep learning models such as convolutional and recurrent neural networks, transformer-based architectures, explainable AI techniques for transparency, and digital twin technologies for system-level modeling and optimization.

To guide the review, the following research questions are considered: (i) What artificial intelligence techniques are currently applied in smart home energy and water management systems? (ii) How effective are these approaches in achieving energy efficiency, water conservation, and system optimization? and (iii) What are the key challenges, limitations, and emerging research directions in AI-driven smart home optimization?

Review design and protocol

This research was executed as a systematic literature review in accordance with the PRISMA 2020 principles, guaranteeing transparency, reproducibility, and methodological precision. The review protocol was established before the literature search and delineates the research objectives, inclusion and exclusion criteria, search strategy, data extraction methodology, and synthesis methodologies. The protocol can be provided upon request or registered if necessary.

The review encompasses peer-reviewed journal articles, conference papers, and technical reports published from 2021 to 2025. Sources outside this timeframe were omitted to guarantee the relevance of the findings. This methodology guarantees a thorough and organized delineation of tasks within the realms of smart home energy, smart water management, load monitoring, reinforcement learning-based home energy management systems, and IoT security and privacy. The review was conducted in accordance with PRISMA guidelines, and a completed PRISMA checklist is provided as Supplementary Material.

Information sources and search strategy

We searched Google Scholar and IEEE Xplore. Google Scholar was selected due to its broad indexing coverage, which includes publications from major databases such as Scopus and Web of Science. To further strengthen coverage, additional studies indexed in Scopus and Web of Science were manually incorporated. Additional sources (e.g., manual citation tracking, reference lists) were used to identify studies not captured by database searches. Google Scholar was used as a primary search engine due to its extensive indexing of peer-reviewed publications across major databases such as Scopus, Web of Science, ScienceDirect, and SpringerLink. The review was updated during revision to include recent publications up to early 2026.

The search strategy combined keywords related to smart homes, artificial intelligence, and resource management. The refined search string used was:

(“smart home” OR “home energy management” OR “HEMS” OR “home automation”) AND (“artificial intelligence” OR “machine learning” OR “deep learning” OR “reinforcement learning”) AND (“energy management” OR “energy efficiency” OR “water management” OR “water efficiency” OR “NILM” OR “non-intrusive load monitoring” OR “IoT security” OR “privacy”)

Searches were limited to English-language publications between 2021 and 2025, with additional recent studies (including early 2026 publications) incorporated during the revision stage. All database search results were recorded, and duplicates (across and within databases) were removed manually (or using reference management software) before screening. Owing to the fluctuating characteristics of Google Scholar’s indexing and ranking, identical queries may provide differing result counts over time; hence, a systematic screening and eligibility protocol was implemented to maintain consistency in research selection.

Eligibility criteria

Inclusion criteria

Publications between 2021 and 2025.

Studies addressing one or more of the following domains: smart home energy management, smart water/wastewater/home water management, non-intrusive load monitoring (NILM), reinforcement learning or AI-based HEMS control, IoT security or privacy in smart home context.

Peer-reviewed journal articles, conference papers, or technical reports.

Papers presenting empirical results, simulations, frameworks, reviews, or conceptual/architectural proposals relevant to home or residential settings (not strictly industrial or city-scale unless explicitly applicable to homes).

Exclusion criteria

Publications before 2021.

Works not available in English.

Studies focused solely on industrial, commercial, or city-scale water/energy systems without home-level or residential relevance.

Opinion pieces, editorials, non–peer-reviewed content, or inaccessible full texts.

Duplicate publications or redundant conference/journal versions of the same study (only one retained).

Study selection process

The selection process involved the following sequential steps, consistent with PRISMA guidance. Identification—Records obtained from database searches and additional sources (n = 92).

Deduplication—Removal of duplicate records (n removed = 14; records after deduplication = 78).

Title and abstract screening—Initial screening against inclusion/exclusion criteria by reviewing titles and abstracts (records screened = 78; records excluded = 30).

Full-text retrieval and screening—Full texts were retrieved and assessed for eligibility (full-text assessed = 48; full-text excluded = 16).

Final inclusion—Studies meeting all eligibility criteria were included in the review (n = 32).

Also see Figure 1.

Screening was performed independently by two reviewers to reduce selection bias. Disagreements were addressed by dialogue and consensus, and when required, a third reviewer was consulted.

Data extraction and management

A data extraction template was developed to systematically collect relevant information from each included study. Extracted data fields included: authors, year, location/context, smart home domain (energy, water, security, etc.), methodology (e.g., IoT architecture, ML/optimization technique, simulation or field deployment), sample size, performance metrics (e.g., energy savings, water savings, detection accuracy, cost reduction), limitations noted by authors, and relevance to multi-resource optimization. Data extraction was performed independently by two reviewers, and entries were cross-checked to ensure consistency. Any discrepancies were resolved through consensus.

Synthesis methodology

The thematic domains included in this study were generated by a synthesis of deductive and inductive methodologies. A collection of preset categories was initially constructed based on the functional architecture of smart home systems and the research objectives. An iterative evaluation of the included papers was conducted to uncover reoccurring themes and patterns, facilitating the refining of these categories. This hybrid methodology guarantees that the theme framework is both theoretically robust and practically substantiated by existing literature.

Due to the heterogeneity of the included studies, characterized by variations in technologies, contexts, evaluation metrics, and resource focus, a narrative (qualitative) synthesis approach was employed. Extracted findings were categorized under the predefined thematic domains (smart home energy, NILM, RL-HEMS, smart water, IoT security), facilitating comparisons of methods, outcomes, strengths, and limitations across studies. Patterns, trends, and research gaps were identified and consolidated. Given the diversity of study designs and reported metrics, a meta-analysis was impractical. Consequently, results are synthesized in thematic tables and discussed qualitatively.

Risk of bias/quality assessment

The diversity of the included papers, which included experimental implementations, simulation-based analyses, conceptual frameworks, and review articles, rendered conventional risk-of-bias evaluation techniques often utilized in clinical or statistical meta-analyses inapplicable. A qualitative assessment approach was utilized to appraise study quality and potential bias. Each study was evaluated according to the following criteria: (i) methodological transparency and clarity of the proposed approach, (ii) adequacy and relevance of evaluation metrics, (iii) validity and consistency of reported results, (iv) completeness of data and experimental description, and (v) acknowledgment of limitations and external validity. The criteria were uniformly used across all included studies and guided the comparative analysis and discussion of results. This method guarantees a contextually relevant and methodical assessment of study quality while preserving flexibility to accommodate diverse research designs.

The five thematic areas explored in this analysis are based on actual trends from the first literature screening and smart home system functional architecture. Smart home energy management solves energy efficiency, cost, and grid interface problems, making it the principal application domain. Non-intrusive load monitoring (NILM) is included because it enables fine-grained energy data collecting and appliance-level intelligence, which enable optimization. Advanced adaptive decision-making methods like reinforcement learning-based control can handle uncertainty and dynamic system behavior. Smart water management is included to move beyond energy-centric approaches and recognize the growing importance of connected energy–water systems in sustainable residential environments. In conclusion, IoT security and privacy are crucial to system stability, user confidence, and widespread adoption. A linked framework reflects the technological, operational, and socio-technical aspects of modern smart home systems. Note that in this results and discussion section, studies with limited methodological detail were interpreted with caution. Table 1 consists of the study of included studies by thematic domain, while Figure 2 gives the distributions of all the publications across the five thematic areas. As observed from Table 1, machine learning-based approaches dominate the literature, followed by reinforcement learning and emerging deep learning techniques.

Smart home energy management

Much of the examined research showed how IoT-enabled sensors, automated control, and optimization algorithms can lower household energy demand, making smart home energy management systems (HEMS) the largest thematic area (Shashank et al., 2025), (Teja et al., 2025). Numerous studies concentrated on combining sensors and actuators for contextual decision-making, such as fault detection, lighting automation, occupancy detection, appliance control, and environmental monitoring. In order to reduce wasteful energy use, for instance, rule-based control techniques that switch appliances based on real-time contextual data were made possible by systems that integrated temperature, humidity, motion, and ambient light sensors (Shashank et al., 2025), (Teja et al., 2025). Other implementations reported energy savings of up to 70% in dynamic load scenarios and used fuzzy logic controllers and adaptive algorithms to enhance decision-making in uncertain home contexts (Babu et al., 2025). As summarized in Table 1, the majority of studies in this domain employ machine learning and IoT-based approaches, with both experimental and simulation-based evaluations.

Recent studies show that reinforcement learning and hybrid methods save energy by over 20% in a big meta-analysis of smart building AI applications. The study found limited real-world deployment and poor integration of numerous building subsystems, showing a mismatch between theoretical performance and practical execution (Houshangi, 2025), (Gunasinghalge et al., 2025). A very accurate artificial neural network-based model predicted and optimized domestic energy consumption, improving lighting and HVAC control. This shows that data-driven predictive models can boost energy efficiency and occupant comfort (Alayed et al., 2025). An AI-driven smart home IoT framework optimizes appliance energy usage using real-time data collecting and machine learning and deep learning models. The technology improves energy efficiency and cost savings while resolving interoperability and data privacy issues (Dada et al., 2024). A thorough assessment of smart home energy management models shows predictive control, renewable integration, and demand-side management improvements. AI-driven optimization and system integration still need improvement, indicating the need for more robust intelligent frameworks (Jamadarkhani et al., 2025). A rigorous study of smart building IoT applications demonstrates that sensor-based systems can dramatically cut energy usage and operational costs. However, high initial costs, integration difficulty, and security concerns prevent widespread implementation (Durrani et al., 2025). An AI-based load forecasting and demand response system shows that deep learning models, especially LSTM, increase prediction accuracy and energy optimization. Results show that predictive analytics and demand response are essential for smart home energy management (Rehman et al., 2025). A review of AI and machine learning technologies for smart home energy optimization demonstrates that regression, clustering, and deep learning cut energy use and enhance system adaptability. The report also finds scalability, data security, and implementation cost issues (Poyyamozhi et al., 2024).

Several studies (see Table 1) report significant energy savings, and investigated data analytics for anomaly detection and real-time energy visualization. To provide energy-use trends, load identification, and appliance performance diagnostics, cloud dashboards were connected with non-invasive sensing modules such smart meters, current transformers, and voltage sensors (Shashank et al., 2025), (Teja et al., 2025). Through dashboards, messaging apps like WhatsApp, or mobile applications, these technologies enhanced user awareness and enabled remote appliance control (Sanjay et al., 2024). When taken as a whole, the studies show substantial potential for energy savings in low-cost prototypes, intelligent sensor networks, and hybrid IoT–AI systems. Nevertheless, the majority of implementations continue to have a narrow scope, concentrating only on power use without fully integrating water usage, heating/cooling, or multi-objective optimization. Emerging trends also include edge AI for low-latency decision-making, federated learning for privacy-preserving optimization, multi-agent reinforcement learning for coordinated control of distributed devices, and hybrid optimization techniques such as genetic algorithms and swarm intelligence, which enable efficient multi-objective decision-making in complex smart home environments.

Non-intrusive load monitoring (NILM)

As summarized in Table 1, only a limited number of studies explicitly address NILM; however, several works incorporate related functionalities such as load disaggregation, consumption profiling, and pattern recognition. Systems utilizing smart metering, current/voltage waveform monitoring, and machine learning-based pattern extraction (Babu et al., 2025), (Shashank et al., 2025) offer essential functionalities for Non-Intrusive Load Monitoring, even if not expressly referred to as such. Energy monitoring platforms that utilize power signatures, instantaneous consumption estimates, or multi-year household datasets demonstrate the capacity for NILM-driven insights to enhance device scheduling, identify inefficient appliances, and reveal behavioral waste. These observations are consistent with the studies outlined in Table 1, which demonstrate that NILM-related techniques are often embedded within broader energy monitoring and analytics frameworks.

Research utilizing deep learning frameworks, such as DBN-based smart metering in Abstract 6, demonstrates a growing focus on pattern categorization and consumption clustering, both essential components of NILM pipelines. Numerous systems additionally indicated the incorporation of load detection alongside real-time notifications for electrical anomalies or irregular usage (Teja et al., 2025), a feature that closely corresponds with NILM’s event detection capacity. Although NILM-specific research is scarce, numerous reviewed papers include methodological elements, including high-resolution metering, feature extraction, and model-based categorization, which establish a foundation for future NILM investigations in low-income settings. The lack of extensive annotated datasets for African homes continues to be a significant obstacle. Overall, Table 1 indicates that while foundational components of NILM exist within the literature, dedicated and large-scale implementations remain limited, particularly in resource-constrained environments.

Reinforcement learning (RL) – Based HEMS control

As shown in Table 1, reinforcement learning-based approaches represent a smaller but increasingly important subset of smart home optimization strategies. The investigations utilizing reinforcement learning or analogous adaptive control methods exhibited the capacity of learning agents to perpetually refine control actions amidst ambiguity. Examples encompassed predictive appliance scheduling, adaptive HVAC regulation, and decision-making influenced by fluctuating environmental variables (Teja et al., 2025), (Guenfaf and Zafoune, 2023). The studies summarized in Table 1 further reveal that RL-based methods are primarily evaluated in simulation environments, with limited real-world deployment.

Numerous systems employed reinforcement learning techniques implicitly, even when reinforcement learning was not expressly mentioned. For example, fuzzy logic adaptive systems (Babu et al., 2025)and predictive rule-based controllers that enhance tactics over time exemplify reinforcement-driven behavior. Reinforcement Learning (RL) has been demonstrated to facilitate dynamic load shifting, peak avoidance, and the optimization of cost, comfort, and performance objectives. Documented enhancements varied from 15% to 25% savings in research focused on learning-based energy scheduling. Nevertheless, the most of the examined IoT prototypes were deficient in RL integration owing to the intricacy of implementing learning agents on resource-limited microcontrollers.

A significant gap noted is the lack of reinforcement learning implementations for multi-resource optimization (electricity and water), which is particularly pertinent to Nigeria, where homes frequently face energy scarcity and inconsistent water supplies. Table 1 also highlights a significant gap in the application of RL for integrated energy–water optimization, suggesting an important direction for future research.

Smart water management

As presented in Table 1, smart water management studies are fewer in number compared to energy-focused research, but they play a critical role in achieving holistic resource optimization. Systems utilizing soil-moisture sensors for irrigation management (Andrijević et al., 2026) or tank-level monitoring and pump regulation (Hasan et al., 2025) illustrated how IoT may improve water-use efficiency and minimize waste. In the examined studies (Naith and Mancy, 2025), intelligent water monitoring systems frequently included sensors, including soil-moisture probes, ultrasonic level detectors, flow detectors, and leak/gas sensors (Gayathri et al., 2024), to deliver advantages in safety and efficiency. A comprehensive assessment of AI and IoT applications in water systems shows that predictive maintenance and real-time monitoring improve resource efficiency (Ikegwu et al., 2025). The report highlights legislative, ethical, and infrastructure issues that must be addressed for sustained implementation. These findings, as reflected in Table 1, demonstrate that IoT-based monitoring and control systems dominate current water management solutions.

Research on AI, deep learning, and IoT in water resource management could improve distribution efficiency, demand forecasting, and water quality monitoring (Raj et al., 2024). Scalability, interdisciplinary integration, and context-specific adaptation remain issues. Tank-level sensors offer a low-cost, non-intrusive alternative to smart metering for water consumption monitoring. The technique adapts well to intermittent water supply conditions, making it suited for resource-constrained regions (Ed-Dehbi et al., 2025).

Numerous researches have correlated water management with sustainability outcomes. For example, systems integrating IoT with cloud dashboards facilitated real-time monitoring of water tank levels, permitting autonomous pump activation and diminishing dependence on manual operation (Hasan et al., 2025). Gamified feedback systems for water conservation (Rasku et al., 2024) shown behavioral enhancements, especially among younger residents. Notwithstanding these contributions, water management is still inadequately examined in comparison to energy systems. Most prototypes focus solely on irrigation or tank filling, with minimal investigation into optimizing the water-energy nexus, greywater recycling, leak analytics, or integrated reinforcement learning-based decision-making. However, Table 1 indicates that most studies focus on isolated water applications, with limited integration into broader energy–water optimization frameworks.

Privacy and security in IoT smart homes

As summarized in Table 1, privacy and security considerations are consistently identified across studies as critical constraints in smart home system deployment. IoT-based automation systems often involve extensive data capture, appliance use, motion patterns, environmental conditions, and user behavior, which exposes households to cyber threats if improperly secured. Several reviewed works acknowledged potential vulnerabilities in wireless communication channels, weak authentication, or unencrypted device-to-cloud transmissions (Teja et al., 2025) and (Nalini et al., 2025).

Studies incorporating AIoT security frameworks emphasized the importance of anomaly detection, encrypted communication, and device-level authentication to prevent unauthorized access and malicious device manipulation (Dobrojevic and Bacanin, 2022). Systems that depend on GSM or web dashboards (Teja et al., 2025) are particularly vulnerable to spoofing, interception, and unauthorized commands. Additional works highlighted the need for interoperability standards and secure protocols to manage the diversity of IoT devices in modern homes. The studies outlined in Table 1 show that while several approaches propose AI-driven security enhancements, comprehensive end-to-end solutions remain limited.

Overall, privacy/security appears consistently as a limiting factor in smart home adoption, particularly in settings like Nigeria where cybersecurity awareness and regulation remain limited. Existing prototypes provide minimal security layers, making them unsuitable for large-scale deployment without substantial hardening. Inexpensive implementations, such as control systems based on WhatsApp, further illustrate the viability of deploying smart home solutions in resource-limited settings. Overall, Table 1 highlights that unresolved security and privacy challenges continue to hinder large-scale adoption of smart home technologies.

Research gaps

Despite significant advancements in smart home technologies for energy and water management, several gaps persist across the literature that limit translation from experimental prototypes to scalable real-world deployment. First, although numerous studies propose IoT- and ML-based energy management systems, a large proportion rely on simulation datasets or controlled laboratory environments rather than longitudinal, in-home data collection. Very few studies deploy sensors and data acquisition systems across multiple real households, creating a gap between algorithmic performance and real-world behavioral variability. This limitation is particularly evident in machine learning–driven works, where models trained on foreign datasets may not generalize to homes with different occupancy patterns, climate conditions, or appliance types.

A second gap pertains to the integration of energy and water management into unified optimization frameworks. While energy-focused studies are relatively mature, water is often addressed separately and primarily from a monitoring or leak-detection perspective. Few studies explore joint optimization of energy–water interactions, such as scheduling water heating, pump management, or water-treatment loads alongside electrical loads. Similarly, multi-objective optimization, balancing energy cost, water usage, comfort, and system longevity remain underdeveloped, with most existing approaches addressing a single objective at a time.

A third significant gap lies in the limited use of advanced reinforcement learning approaches in real-world deployments. Although RL-based HEMS systems show great promise, many implementations remain theoretical or require large, high-frequency datasets that are difficult to collect in typical homes. Practical issues such as computational cost, safety during exploration, and constraints imposed by user habits are rarely addressed.

Privacy and security research also shows important shortcomings. Several studies propose anomaly detection or authentication mechanisms, yet few frameworks provide end-to-end security, including secure data transmission, access control, device interoperability, and user privacy transparency. Moreover, explainability, critical for user trust, is only addressed in isolated studies.

The examined studies indicate considerable potential for enhancing energy efficiency and water conservation; however, there is a conspicuous deficiency of thorough economic and environmental effect evaluations in the literature. Most deployments emphasize technical performance measures, like energy savings, detection accuracy, and system responsiveness, while providing minimal reporting on installation costs, ongoing expenses, return on investment, or payback periods. This disparity hinders a comprehensive assessment of the practical viability of various technologies, especially in resource-limited settings. Nonetheless, existing research indicates that inexpensive IoT sensors, smart meters, and microcontroller-based systems offer comparatively economical entry points, while energy savings of 10%–70% suggest the possibility of cost recovery over time, contingent upon usage patterns and local tariffs. From an ecological standpoint, lower energy usage.

Finally, there is a noticeable geographical and contextual research gap. Most studies originate from high-income countries with stable electricity supply, advanced digital infrastructure, and high IoT adoption rates. There is limited research tailored to sub-Saharan Africa, low-resource environments, or regions with irregular water and electricity supply, despite these being the areas where optimization technologies may have the highest impact. This gap underscores the need for context-specific algorithms, affordable hardware designs, and socio-technical frameworks that consider behavioral, economic, and infrastructural constraints. These limitations highlight the gap between experimental validation and real-world deployment, particularly in resource-constrained household environments.

Implications for Nigeria

This review’s conclusions have significant significance for Nigeria, where issues including unstable electricity, increasing urbanization, water shortages, and minimal automation adoption necessitate urgent intelligent resource management solutions. Properly adapted smart home energy and water optimization technology could substantially advance national objectives related to sustainability, cost efficiency, and enhanced living standards.

The prevalence of IoT-based energy management systems in the literature indicates that Nigeria could gain from scalable, cost-effective sensor networks for monitoring residential usage. These devices can aid households in optimizing generator utilization, solar hybrid systems, and grid electricity, thus minimizing waste and enhancing load balancing. Nigeria’s growing implementation of rooftop solar systems is effectively complemented by reinforcement learning-based and predictive control approaches, which can enhance the timing of appliance operation in relation to solar availability and tariff frameworks.

The systematic assessment underscores the significance of NILM technologies, especially in Nigeria, where the installation of several smart plugs or metering points may be economically unfeasible. NILM allows utilities, researchers, and families to comprehend appliance-specific usage without supplementary gear, rendering it suitable for extensive energy diagnostics across many socio-economic contexts.

The robust presence of smart water management research suggests an opportunity for Nigeria to advance IoT-enabled water monitoring, leak detection, and automated pump control systems. Considering the frequent utilization of boreholes, sporadic municipal water supply, and dependence on overhead and subterranean storage tanks, intelligent water systems could mitigate waste, decrease pumping expenses, and guarantee a more dependable residential supply.

The paper emphasizes the necessity of localized datasets, which are crucial for training precise machine learning and reinforcement learning models. This necessitates Nigeria’s investment in field deployments, multi-home monitoring, and partnerships among universities, utilities, and smart technology firms. Data gathered locally will facilitate the creation of optimization algorithms tailored to Nigeria’s distinct climatic patterns, load characteristics, occupancy schedules, and socio-cultural practices.

Nonetheless, privacy and security issues require meticulous attention. With the proliferation of IoT devices, Nigerian families may be susceptible to unwanted entry or data exploitation. Establishing robust security standards, encrypted communication, and transparent user consent methods will be crucial for fostering public trust and promoting adoption.

The findings suggest that Nigeria should pursue solutions that are cost-effective, durable, and resilient in contexts characterized by unpredictable power supply and uneven internet access. This facilitates prospects for edge computing, offline-capable systems, and energy-efficient device designs customized to local conditions.

This systematic analysis emphasizes the swiftly changing domain of data-driven smart home technologies and underlines their significant potential for enhancing sustainable energy and water management, especially in resource-limited environments. Research indicates that smart home energy management systems utilizing IoT sensors, machine learning, and optimization methods can markedly decrease household energy use via real-time monitoring, adaptive device scheduling, and predictive analytics. NILM research advances appliance-level disaggregation without the need for intrusive hardware, hence improving the granularity of household energy information and facilitating more tailored automation solutions. Reinforcement learning-based Home Energy Management Systems (HEMS) represent a significant advancement, as numerous studies demonstrate the capacity of RL agents to acquire optimal control policies that reconcile comfort, cost, and efficiency; however, challenges persist concerning sample efficiency, interpretability, and deployment stability. Simultaneously, intelligent water management solutions, however less developed, exhibit promising potential for leak detection, demand forecasting, and integrated energy-water optimization. A prevalent theme across various domains is that IoT-enabled smart homes present significant privacy and cybersecurity challenges due to ongoing data collection, interoperability among multiple devices, and network vulnerability, underscoring the imperative for secure communication protocols, authentication systems, and privacy-preserving analytics. The findings collectively indicate significant technological advancement while also underscoring enduring deficiencies, such as inadequate research in African and emerging nations, a lack of comprehensive multi-objective optimization frameworks, and a scarcity of long-term real-world implementations. These deficiencies highlight the necessity for context-specific, cost-effective, and scalable solutions designed for regions like Nigeria, where infrastructural disparities, energy-water shortages, and socio-economic limitations influence technology adoption. This research establishes a comprehensive basis for enhancing AI-driven smart home systems to achieve more efficiency, security, and sustainability in domestic resource management. Many of the reviewed approaches are particularly relevant to developing regions such as Sub-Saharan Africa, the Middle East, and South Asia, where challenges related to energy reliability, water scarcity, and infrastructure constraints necessitate cost-effective and scalable smart home solutions. Low-cost IoT systems and adaptive AI-based control strategies identified in this review are especially suitable for deployment in such environments.