Soil organic carbon stocks (SOCS) are critical components of the global carbon cycling and play a central role in climate change mitigation. However, their dynamics in high‐altitude Andean ecosystems remain poorly understood despite their importance for carbon sequestration. The significant spatial heterogeneity of SOCS in mountainous terrain makes accurate quantification and mapping challenging. This study evaluated the performance of geospatial regression and machine learning (ML) approaches for predicting SOCS in two Peruvian Andean basins: Torobamba and Coata. We compared Geographically Weighted Regression (GWR), GWR with collinearity analysis (GWRC), their kriging‐adjusted variants, and ML models (Random Forest, Gradient Boosting). Models were built using key SOCS covariates for each basin and validated through 5‐fold cross‐validation with Root Mean Square Error (RMSE), Mean...