This study aims to delineate groundwater potential zones (GWPZ) in the Gidabo watershed of the Main Ethiopian Rift using an integrated approach of Geographical Information System (GIS), Remote Sensing (RS), and the Analytical Hierarchy Process (AHP). The study area covers an area of 809.9 km² and is characterized by diverse geological, topographical, and climatic conditions. Eight groundwater regulating factors, including rainfall, elevation, drainage density, soil types, lineament density, slope, lithology, and land use/land cover (LULC), were analyzed. The AHP method was used to assign weights to each factor based on their relative importance in groundwater occurrence. The weighted overlay analysis (WOA) technique was then applied in the ArcGIS environment to integrate all thematic layers and generate a GWPZ map. The GWPZ was classified into five categories: poor, low, moderate, high, and very high. The results showed that 18.7% of the area is classified as poor GWPZ, 33.8% as low GWPZ, 23.4% as moderate GWPZ, 18.1% as high GWPZ, and 5.8% as very high GWPZ. The model's accuracy was validated using well, borehole, and spring data through ROC and AUC analysis, achieving a good accuracy of 76.8%. The study highlights the importance of lithology as the most dominant factor regulating groundwater potential and suggests that water policymakers should prioritize developing groundwater resources in favorable GWPZs to enhance agricultural productivity and domestic water supply. Future research should focus on detailed aquifer characterization studies to optimize groundwater management and abstraction schemes.This study aims to delineate groundwater potential zones (GWPZ) in the Gidabo watershed of the Main Ethiopian Rift using an integrated approach of Geographical Information System (GIS), Remote Sensing (RS), and the Analytical Hierarchy Process (AHP). The study area covers an area of 809.9 km² and is characterized by diverse geological, topographical, and climatic conditions. Eight groundwater regulating factors, including rainfall, elevation, drainage density, soil types, lineament density, slope, lithology, and land use/land cover (LULC), were analyzed. The AHP method was used to assign weights to each factor based on their relative importance in groundwater occurrence. The weighted overlay analysis (WOA) technique was then applied in the ArcGIS environment to integrate all thematic layers and generate a GWPZ map. The GWPZ was classified into five categories: poor, low, moderate, high, and very high. The results showed that 18.7% of the area is classified as poor GWPZ, 33.8% as low GWPZ, 23.4% as moderate GWPZ, 18.1% as high GWPZ, and 5.8% as very high GWPZ. The model's accuracy was validated using well, borehole, and spring data through ROC and AUC analysis, achieving a good accuracy of 76.8%. The study highlights the importance of lithology as the most dominant factor regulating groundwater potential and suggests that water policymakers should prioritize developing groundwater resources in favorable GWPZs to enhance agricultural productivity and domestic water supply. Future research should focus on detailed aquifer characterization studies to optimize groundwater management and abstraction schemes.