【Objective】 Effective soil thickness is a decisive indicator for evaluating soil health and productivity, it is therefore of great significance to accurately depict the spatial distribution pattern of effective soil thickness and its response mechanism to land use change and surface substrate type for the sustainable protection of soil resources. 【Method】 In this study, leveraging on the surface substrate survey data and soil-landscape modeling, we carried out predictive modelling and mapping of effective soil thickness in the black soil area of eastern Inner Mongolia. Based on the modelling results, the spatial variability of effective soil thickness was analyzed among land use types and surface substrate zoning. SHAP analysis was employed to identify the main controlling factors underlying the spatial distribution pattern of effective soil thickness. 【Result】 The results show that the Cubist-based regression model had a good performance(R²=0.5, RMSE=43.8) for effective soil thickness prediction, and the generated spatial distribution map could accurately reveal its spatial pattern. SHAP analysis revealed that topographic and climatic factors were the main controlling factors determining the spatial variability of effective soil thickness, which was specifically reflected in the fact that highly eroded areas with high elevation had thinner soils, while monthly average temperature extremes had a positive effect. 【Conclusion】 Both surface substrate zoning and land use types exerted important constraints on the spatial characteristics of the effective soil layer thickness, with the overall soil layer thickness in the floodplain and sloping deposit areas being greater than that in the residual slope deposit area. Forestlands, which were mostly distributed in mountainous areas or regions with steep slopes, had the thinnest effective soil layer. This study provides a methodological reference for the spatial modeling and characterization of effective soil thickness, and the results can provide a data basis for identifying the background conditions of regional natural resources and their response mechanisms to human interactions.