This webinar reimagines urban water infrastructure as a dynamic system where Artificial Intelligence (AI) helps manage complexity through interpretable and adaptive models. We explore how machine learning, network analytics, and AI-driven digital twins reveal critical interdependencies, feedback loops, and decentralised behaviours, enabling more resilient and adaptive operations. Case studies will illustrate this practical shift, including DMA optimisation to enhance network resilience and efficiency, data-driven approaches for operational decision-making, and the development of AI-driven digital twin frameworks. The session concludes with a forward look at emerging opportunities for autonomous, self-learning water systems, powered by AI, real-time data, and distributed intelligence.