Efficient management of water resources is essential for sustainable development. In the context of computer-based analysis in resource management, geospatial technology, specifically the incorporation of remote sensing (RS)-geographic information system (GIS) is exceptionally effective. This study seeks to investigate the application of RS-GIS and artificial intelligence (AI) technologies in critical water resource management practices to improve their efficacy and efficiency. It delineates the approaches proposed for modeling water resources, including Random Forest, support vector machine, multilayer perceptron neural networks, and long short-term memory, across different water-related domains including potential groundwater mapping, rainfall forecasting, and surface water evaluation. This study highlights the diverse potentials and challenges of water resource modeling, focusing on enhancing modeling accuracy and efficiency, with a comprehensive assessment of the use of AI and GIS in water resource management. The integration of RS-GIS and AI facilitates continuous monitoring of water resources, providing valuable recommendations for efficient water resource management for achieving sustainable development goal.