The integration of machine learning (ML), artificial intelligence (AI), and the Internet of Things (IoT) into water resource management has transformed conventional approaches and enabled intelligent, data-driven, and scalable solutions. These technologies leverage vast datasets, leading-edge computational models, and sensor networks to manage proactive water governance. AI and ML demonstrated exceptional capabilities in hydrological modeling, flood prediction, groundwater analysis, and forecasting water demand, while IoT-tied systems provide real-time monitoring of water quality, distribution efficiency, and leakage detection, allowing for remote infrastructure management. The blend of AI with IoT-generated data powers predictive analytics, pattern recognition, and adaptive decision-making, thereby enhancing the accuracy of flood and drought predictions and optimizing agriculture water use through smart irrigation systems based on real-time data inputs. Additionally, AI-based methods contribute to wastewater treatment and sustainable water recycling by increasing operational efficiency and management precision. However, such advances come with challenges that involve privacy concerns about data, demands on computation, fulfillment of regulatory requirements, and scalability of the infrastructure. Ethical considerations also arise regarding the equitable allocation of limited water resources. Addressing these challenges requires multidisciplinary collaboration and continued research to refine these technologies and ensure their integration into sustainable, efficient, and adaptive global water management frameworks.