Soil erosion is viewed as a major environmental crisis in the Upper Ganga Watershed, especially in the hilly area of the Kumaon Himalayan region. The study applies the Revised Universal Soil Loss Equation (RUSLE) and the Modified Universal Soil Loss Equation (MUSLE) in the context of a Geographic Information System (GIS) to examine spatial patterns of soil erosion and prioritize sub-watersheds for conservation. By accessing and integrating satellite datasets (CHIRPS precipitation dataset, OpenLandMap soil dataset, SRTM elevation dataset, MODIS Land use land cover (LULC) dataset, and Sentinel-2 NDVI dataset) with existing datasets to derive factors driving future soil erosion (rainfall erosivity (R), soil erodibility (K), topographic factor (LS), cover-management (C) and support practice (P)), RUSLE predicted average annual soil loss of 4327.72 t ha⁻1 yr⁻1 with 19.5% of the area under very high erosion risk. MUSLE predicted average sediment yields of 18.50 t/ha/yr from runoff-driven erosion, with 21.6% of the area under very high risk. Sub-watersheds WS3 and WS6 exhibited very high erosion risk across both models, whereas WS10 had the lowest risk. Correlation analyses suggest a moderate-strong relationship (r = 0.558, R2 = 0.31) between models must augment (2). Hence, the models complement one another well for the assessment of erosion risk. This research has demonstrated the successful implementation of RUSLE and MUSLE with GIS and remote sensing for assessing the erosion potential at the watershed spatial scale and provides a scientific basis for regional prioritization of sub-watershed areas for sustainable soil and water conservation interventions.