The mountainous areas in China cover a vast territory, with a widespread and increasingly frequent distribution of geological disasters. Obtaining accurate susceptibility assessment results is key to effective disaster prevention and mitigation. To construct a high-precision susceptibility evaluation model and perform interpretability analysis, Fanshi County, Dai County, and Wutai County in Xinzhou City, Shanxi Province were selected as the study area. Based on geographic information systems, 14 influencing factors were chosen, and two indicators—variance inflation factor (VIF) and tolerance (TOL）—were used to conduct multicollinearity tests and factor selection. A susceptibility evaluation system was established. The information method (IM) was used to assist in selecting non-disaster samples, which were then combined with disaster point samples to form a complete dataset. Three machine learning methods, namely backpropagation neural network (BP), random forest (RF), and extreme gradient boosting (XGBoost), were applied to build the susceptibility evaluation models. The model with the best performance was explained using the SHAP algorithm. The results show that the susceptibility evaluation model based on Random Forest performs the best, with the largest area under the receiver operating characteristic (ROC) curve, a more reasonable classification interval, and higher prediction accuracy. The prediction results reveal that areas with very high susceptibility are mainly distributed near mountain roads and rivers, accounting for 21.2% of the total area. SHAP analysis indicates that geological disasters are primarily influenced by eight factors, including terrain undulation, vegetation index, and distance to roads. Dependency analysis and local explanations further reveal the specific impact of feature factors on the prediction results, providing guidance for disaster prevention and understanding disaster occurrence patterns in this region.