A structured review and critical synthesis of multi-criteria decision-making models integrated with machine learning for water resource management

Water resource management (WRM) is increasingly shaped by interconnected environmental, technical, social, and institutional pressures that make single-method analysis insufficient for robust decision support. The study follows a structured review methodology with systematic search and screening, combined with critical thematic synthesis to identify and synthesise studies on integrated MCDM-ML applications in WRM, with emphasis on conceptual integration patterns, domain-specific applications, methodological trade-offs, reporting gaps, and future implementation directions. Critically examining how multi-criteria decision-making (MCDM) models and machine learning (ML) techniques are being integrated to support more effective, transparent and context-appropriate WRM. The review identifies three broad integration patterns, namely sequential, parallel, and fully coupled frameworks. Across groundwater potential and recharge assessment, water demand forecasting and supply planning, flood risk and hydrological hazard management, water quality assessment and pollution control, sediment and catchment management, and urban water loss management, a consistent pattern emerges. ML contributes most strongly to prediction, classification, and pattern detection, while MCDM strengthens criteria weighting, prioritisation, and final decision structuring. The review also shows that although integrated systems often report strong case-specific results, cross-study comparison remains limited by inconsistent performance metrics, uneven validation procedures, weak transparency in weighting structures, limited uncertainty treatment, and poor reproducibility. In response, the paper proposes a minimum reporting framework and highlights key future directions, including explainable and trustworthy AI, real-time and IoT-enabled decision support, cloud and edge deployment, and policy-aligned stakeholder-centred systems. Overall, integrated MCDM-ML approaches show strong potential for WRM, but their future value will depend on clearer reporting, stronger validation, and closer alignment with real decision contexts.