Water resource management is evolving with the use of Artificial Intelligence through predictive, data-rich, and adaptive solutions for global challenges such as water scarcity, pollution, and changing demand. With growth in the global population, urbanization, and pressure from climate change on existing systems, traditional methods are no longer effective for achieving efficiency and sustainability. The use of artificial intelligence methods is also increasing, including the fields of machine learning, deep learning, fuzzy logic, reinforcement learning and hybrid models, for forecasting demand for urban, industrial, and agricultural purposes, identifying water losses, predicting system failure, and reducing water loss. The application of AI for water quality monitoring can also predict parameters like pH, turbidity, and concentration of contaminants. These models could contribute to health of the environment and safety for public health, and agricultural uses such as precision irrigation can maximize water use efficiency while increasing productivity by using soil, crop, weather and sensor data to implement the right irrigation amounts at the right times. AI may support reservoir operation, forecasts of inflow and forecasting drought and floods. Work remains to be done with cost, data quality, or adoption challenges; nevertheless, developments in explainable AI and edge computing can provide the support needed for sustainable resource management of water resources.