The study reflects an understanding of individual factors regulating and controlling the content of organic carbon of soils, and shows a modern quantitative assessment of the content of organic carbon of soils in Russia, taking into account its huge variability. Paper presents the results of three-dimensional modeling of the organic carbon content of soils with 500 m spatial resolution at several standard depths (0–5, 5–15) to the territory of the Russian Federation using ensemble machine learning. Automated predictive mapping was based on 4 961 soil horizons from 863 soil profiles, and an extensive set of spatial information, including bioclimatic variables, a digital elevation model and its derivatives, and long-term averaged time series of MODIS data. The results of spatial cross-validation show lower (when compared with randomized) accuracy: the coefficient of determination is 0.46, CCC 0.63, RMSE 1.41 g/kg. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.