This thesis presents the design, development, and implementation of an intelligent cloud-based infrastructure for smart water distribution monitoring, featuring an innovative conversational AI interface that transforms how operators interact with complex monitoring systems. The research addresses key limitations in current water infrastructure monitoring, where traditional interfaces require technical expertise and hinder effective system use. The implemented solution leverages Google Cloud Platform services to build a comprehensive monitoring ecosystem integrating real-time sensor data processing, hydraulic simulation, machine learning-based leakage detection, and a conversational AI interface. The architecture applies cloud-native principles such as microservices, serverless computing, and event-driven processing to ensure scalability, reliability, and cost-efficiency.