Abstract: [ Objective ] This study aims to establish a three-dimensional mineral prospectivity prediction method that integrates mineralization process numerical simulation and machine learning, using the Maodeng copper-tin deposit in Inner Mongolia as a case study, to address the challenges of deep mineral body prediction. [ Methods ] The study uses Flac3D for numerical simulation of the mineralization process to obtain key physical field parameters that control mineralization, such as stress, temperature, and fluid pressure. These physical results, combined with geological data, are then used in the XGBoost machine learning model for three-dimensional quantitative mineral prospectivity prediction. [ Results ] The results demonstrate that this method successfully simulated the stress field, temperature field, and fluid migration process of the mining area. The AUC value of the XGBoost model reached 99.26%, showing excellent predictive ability. SHAP analysis revealed that shear stress, pore pressure, and temperature are the main factors affecting the distribution of mineral bodies. The predicted results highly correlated with the known mineral bodies, providing a reliable basis for mineral body prediction and ultimately identifying two mineral prospecting target areas. [ Conclusion ] The study demonstrates that the combination of mineralization process numerical simulation and machine learning prediction methods can effectively improve the accuracy of deep mineral resource predictions, offering new technical insights for mineral resource assessment in similar regions.