Toward resilient water infrastructure using artificial intelligence technologies for sustainable recourses of water

Water resource management supported by Artificial Intelligence (AI) is revolutionizing by enabling more efficient, predictive, and sustainable practices amid growing global pressures on water systems. In this article, we focus on the multifaceted applications of AI in water quality monitoring, flood prediction, demand forecasting, and irrigation optimization. We review how AI algorithms integrated with sensor technologies and data analytics allow for real-time water quality assessment, early detection of contamination, and prediction of pollution trends. Machine learning models, including ARIMA, neural networks, decision trees, and deep learning approaches like CNNs and RNNs, demonstrate high accuracy in forecasting water quality parameters and environmental hazards such as floods. The synergy between AI and IoT facilitates continuous high-resolution data collection, significantly enhancing traditional reactive management methods. In flood management, ensemble and deep learning models process meteorological and hydrological data to provide timely forecasts, aiding disaster preparedness. Similarly, AI-driven demand prediction and resource allocation models optimize water distribution, reducing waste and operational costs. In agriculture, soil moisture sensing combined with weather data enables precision irrigation scheduling, promoting water conservation and crop productivity. Despite these advancements, challenges such as data quality, model interpretability, and infrastructure integration remain. Future developments in sensor networks, remote sensing, and deep learning will further refine predictive accuracy and operational efficiency. This review highlights the transformative potential of AI to create resilient, sustainable, and smart water systems worldwide, emphasizing the importance of continued innovation, stakeholder engagement, and policy support to realize these benefits. • AI enables real-time water quality monitoring and pollution detection. • AI improves flood prediction, mapping, and emergency response systems. • Water management benefits from AI-driven demand forecasting and leak detection. • AI optimizes water treatment processes, chemical use, and equipment maintenance. • Future AI advances will enhance remote sensing, data fusion, and autonomous controls.