<![CDATA[Water resource management has become increasingly complex as climate change, population growth, and land-use expansion intensify pressure on global water systems. Integrated Water Resource Management (IWRM) offers a holistic approach to balancing ecological, social, and economic water needs; however, traditional methods often fall short in addressing real-time variability and long-term sustainability challenges. In response, the advancement of Artificial Intelligence (AI) provides transformative opportunities to enhance hydrological prediction, dynamic monitoring, and decision optimization within the IWRM framework. This study aims to (1) explore the potential and challenges associated with AI adoption in sustainable integrated water-resource management, (2) propose a conceptual framework that integrates AI with IWRM through physical modeling, real-time monitoring, and decision-support optimization, and (3) offer technical and policy recommendations for implementing AI in national water sectors, with a specific emphasis on Indonesia. Using a qualitative literature-based methodology, this research synthesizes findings from scientific publications, institutional reports, and empirical case studies. The results indicate that AI significantly improves streamflow forecasting, water-quality prediction, automated system control, and multi-objective water allocation. Despite these benefits, challenges such as data scarcity, limited model interpretability, technological infrastructure gaps, and governance constraints hinder effective implementation. The proposed framework demonstrates how AI can be operationalized across hydrological modeling, sensor-based monitoring, and decision-support engines to strengthen IWRM outcomes. Overall, the study highlights the urgent need for strategic investment, regulatory readiness, and capacity building to unlock AI’s full potential in achieving sustainable and resilient water governance.]]>
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