Highlights What are the main findings? In the arid Akesai region, the Gradient Boosting model (R2 = 0.675, RMSE = 1.304 g kg-1) outperformed other machine learning algorithms in predicting SOC, with vegetation-type factors (NDVI_PC1) and clay content identified as the most influential positive drivers of SOC accumulation. The spatial distribution of SOC exhibits clear heterogeneity, with higher concentrations in mountainous and valley areas, which is primarily governed by the synergistic interplay of vegetation dynamics, soil texture, and topographic features. What are the implications of the main findings? Methodologically, this study demonstrates that combining ensemble machine learning with interpretable SHAP analysis provides a robust and transparent framework for quantifying the contribution of multiple environmental factors to SOC variability in data-scarce arid regions. Practically speaking, the identified key drivers and high-resolution spatial patterns provide a scientific basis for targeted land management, ecological restoration, and more accurate carbon stock assessment in arid ecosystems.Highlights What are the main findings? In the arid Akesai region, the Gradient Boosting model (R2 = 0.675, RMSE = 1.304 g kg-1) outperformed other machine learning algorithms in predicting SOC, with vegetation-type factors (NDVI_PC1) and clay content identified as the most influential positive drivers of SOC accumulation. The spatial distribution of SOC exhibits clear heterogeneity, with higher concentrations in mountainous and valley areas, which is primarily governed by the synergistic interplay of vegetation dynamics, soil texture, and topographic features. What are the implications of the main findings? Methodologically, this study demonstrates that combining ensemble machine learning with interpretable SHAP analysis provides a robust and transparent framework for quantifying the contribution of multiple environmental factors to SOC variability in data-scarce arid regions. Practically speaking, the identified key drivers and high-resolution spatial patterns provide a scientific basis for targeted land management, ecological restoration, and more accurate carbon stock assessment in arid ecosystems.Abstract Soil organic carbon (SOC) plays a critical role in the terrestrial carbon cycle, yet its spatial patterns and drivers in arid regions remain poorly understood. This study aims to clarify SOC distribution mechanisms in the Akesai region, where limited water-heat conditions and land use create high environmental heterogeneity. Four machine learning models were applied to predict SOC content and produce high-resolution spatial maps, and SHAP analysis was used to quantify the contributions of key environmental variables. The Gradient Boosting model had the best performance (R2 = 0.675; RMSE = 1.304 g kg-1), followed by XGBoost, LightGBM, and Random Forest. The results indicated that the main factors controlling SOC variation were NDVI, DEM, sand, clay, mean temperature, and ERVI. Furthermore, NDVI and clay parameters were positively associated with promoted SOC accumulation, while sand showed a negative effect. Spatially, higher SOC values were found in mountainous zones and vegetated valleys, while low SOC values were observed in flat, arid plains. These findings demonstrate that incorporating vegetation-type indicators substantially improves large-scale SOC estimation and enhances our understanding of SOC spatial dynamics and the driving mechanisms in arid environments. This provides a scientific basis for carbon-stock assessment and sustainable land management.