Groundwater is a vital freshwater resource facing growing stress due to climate change, overexploitation, and weak public understanding. Despite its central role in sustaining ecosystems, agriculture, and human consumption, groundwater remains under-monitored and under-studied. This paper presents a predictive modeling workflow for estimating groundwater recharge (GWR) using artificial intelligence (AI) and satellite-based remote sensing (RS) data. The proposed approach integrates data acquisition, preprocessing, fusion, scaling, normalization, and supervised learning with algorithms such as Random Forest and XGBoost. Influencing factors like precipitation, evapotranspiration, soil moisture, and hydrological indicators are included. Experimental results from Morocco demonstrate high prediction accuracy, showcasing the scalability and applicability of this AI-RS workflow for sustainable water resource management and decision-making.