<![CDATA[Groundwater underpins water security for households, agriculture, industry, and ecosystems across much of the world. Rising demand, climatic stress, and sustained abstraction have therefore increased the need for exploration methods that are both dependable and efficient. Remote sensing (RS) and geographic information systems (GIS) now play a central role in groundwater potential mapping because they allow geological, topographic, climatic, hydrological, and land-surface information to be examined together across large areas. More recently, machine learning (ML), artificial intelligence, cloud computing, and Google Earth Engine (GEE) have expanded the speed, accuracy, and spatial reach of these assessments. This review examines the recent development of RS–GIS-based groundwater exploration, with particular attention to the shift from conventional multi-criteria decision-making methods toward data-driven and explainable artificial intelligence (XAI). It evaluates commonly used groundwater conditioning factors, compares major predictive approaches, and discusses persistent limitations involving data quality, uncertainty, validation, interpretability, and model transferability. The review also considers emerging directions, including GeoAI, Digital Twins, cloud-based geospatial analysis, and hybrid modeling, and assesses their potential contribution to more sustainable groundwater management.]]>
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