Artificial Intelligence in Smart Water Management: Emerging Technologies, Applications, Challenges, and Future Directions

DOI

https://www.doi.org/10.59256/ijrtmr.20260604041

Published In

Volume 6, Issue 4 (2026)

Published Online

July-August 2026

Pages

374-380

Article Type

Review Article

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Artificial intelligence (AI) is rapidly transforming water management by enabling data-driven prediction, intelligent monitoring, optimization, and decision support across water-resource systems. This review critically examines the emerging applications of AI in smart water management, with particular emphasis on the transition from conventional monitoring and prediction toward integrated decision-making and autonomous system control. The review synthesizes recent developments in machine learning, deep learning, Internet of Things (IoT)-based systems, reinforcement learning, explainable artificial intelligence, and emerging agentic AI approaches applied to water resources, water-quality monitoring, groundwater management, hydrological forecasting, reservoir operation, wastewater treatment, water- demand prediction, and smart water infrastructure. The major benefits of AI include improved prediction accuracy, real-time monitoring, early detection of water-quality deterioration, optimization of operational processes, and enhanced decision support. However, widespread implementation remains constrained by data quality and availability, model interpretability, computational requirements, cybersecurity risks, infrastructure limitations, high implementation costs, and regulatory and governance challenges. Particular attention is given to the emerging transition from AI- assisted decision support toward autonomous water-system control, where AI models can increasingly connect sensing, prediction, decision-making, and physical actions. The review further identifies key research gaps related to trustworthy and explainable AI, integration of heterogeneous data sources, physics-informed machine learning, human-in-the-loop decision-making, and equitable deployment in resource-constrained water systems. Finally, a conceptual framework is proposed linking sensing, AI-based prediction, decision support, autonomous control, and continuous feedback, providing a pathway toward resilient, intelligent, and sustainable water management systems.