Water scarcity has emerged as one of the most pressing global challenges of the twenty-first century, driven by rapid urbanization, population growth, climate change, industrial expansion, and increasing agricultural demands. According to international estimates, global water demand is projected to increase by approximately 20–30% by 2050, while nearly half of the world's population may experience water stress conditions. Consequently, accurate water demand forecasting has become a critical component of sustainable water resource management and conservation planning. Traditional statistical forecasting techniques, including autoregressive integrated moving average (ARIMA), regression analysis, and time-series models, have demonstrated limitations in capturing the nonlinear, dynamic, and multidimensional factors influencing water consumption patterns. Recent advancements in machine learning (ML) and artificial intelligence (AI) have introduced innovative approaches capable of improving forecasting accuracy and supporting data-driven water conservation strategies.

This systematic literature review examines the application of machine learning techniques in water demand forecasting and their contribution to efficient water conservation. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, the study synthesizes findings from major peer-reviewed publications published between 2015 and 2026. The review analyzes key machine learning algorithms, including Artificial Neural Networks (ANN), Support Vector Machines (SVM), Random Forests (RF), Extreme Gradient Boosting (XGBoost), Long Short-Term Memory (LSTM) networks, and hybrid deep learning models. Furthermore, the paper evaluates the strengths, limitations, and practical implications of these approaches across urban, agricultural, industrial, and smart water management contexts. The findings indicate that deep learning and hybrid machine learning frameworks consistently outperform conventional forecasting techniques, particularly in complex environments characterized by high variability and climate uncertainty. The review concludes by identifying critical research gaps, emerging trends, and future directions for integrating machine learning, Internet of Things (IoT), digital twins, and explainable artificial intelligence into next-generation water conservation systems.

Keywords: Water Demand Forecasting, Machine Learning, Water Conservation, Deep Learning, Smart Water Management, Artificial Intelligence, Sustainable Resource Management