My research focuses the transformative integration of artificial intelligence (AI) and data science with urban water systems engineering. I aim to develop next-generation intelligent systems for water distribution networks, with a core focus on leveraging machine learning, deep learning, and graph-based algorithms to solve fundamental challenges. My work specifically explores: AI-driven mechanisms for leakage detection and diagnostics through multi-feature fusion; graph neural networks and network embedding techniques for adaptive hydraulic state estimation and pressure sensor optimization; and deep learning models for pipe burst identification and localization. By treating water networks as dynamic, data-rich systems, I develop methodologies that enable predictive maintenance, intelligent scheduling, and enhanced operational resilience, pushing the boundaries of smart water management.