Water leakage in urban distribution networks is a huge test, prompting asset misfortune, expanded functional expenses, and ecological corruption. Customary recognition techniques, which depend on manual reviews and basic sensors, frequently neglect to distinguish spills in a convenient and practical way. This paper presents an artificial intelligence fueled water leakage monitoring system intended to resolve these issues. The framework uses advanced machine learning algorithms like anomaly detection, deep learning models, and predictive analytics to handle information from IoT-empowered sensors deployed all through the water network. By ceaselessly dissecting boundaries, for example, water pressure, stream rates, and natural circumstances, the simulated intelligence models can recognize even little breaks continuously and anticipate potential disappointment focuses before they happen. This proactive methodology empowers prescient upkeep, decreasing water wastage and bringing down functional expenses. We investigate the system’s architecture, including its AI-driven analytics engine, real-time data processing, and communication networks for efficient data transfer. Carried out in metropolitan water organizations, this arrangement upgrades infrastructure sustainability, streamlines water use, and supports the advancement of more astute, greener urban areas. The paper concludes with an assessment of the framework's presentation, adaptability, and potential for broad reception.