<![CDATA[Rapid urbanization in Southeast Asia has exerted unprecedented pressure on water resources, leading to inefficiencies, resource depletion, and challenges in maintaining water quality. Conventional water management approaches often struggle to meet dynamic demand patterns and respond to infrastructure constraints, limiting sustainable urban water governance. This study aims to evaluate the role of artificial intelligence-driven predictive analytics in enhancing water resource management by forecasting demand, detecting system vulnerabilities, and optimizing allocation strategies in rapidly growing urban regions. A mixed-methods research design was employed, integrating quantitative hydrological and consumption datasets with real-time sensor data, machine learning-based predictive modeling, and qualitative expert insights. Data were analyzed through scenario-based simulations, regression analysis, and cross-validation to assess predictive performance and operational effectiveness. Results indicate that AI-enabled predictive analytics significantly reduces non-revenue water from 32% to 19%, improves reservoir stability from 68% to 81%, enhances water quality indices from 74 to 88, and increases leakage detection from 45% to 78%. Case studies demonstrate the practical applicability of predictive alerts in proactive infrastructure management and resource optimization. The study concludes that AI-driven predictive analytics provides a transformative tool for sustainable urban water governance, enabling proactive, efficient, and adaptive management strategies in complex urban environments.]]>
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