The assessment of wildfire susceptibility is crucial for the early prevention of wildfires and the development of disaster management strategies. Currently, research on wildfire susceptibility mainly focuses on improving the predictive accuracy of models while often neglecting the analysis and interpretation of the internal decision mechanisms of the models. Therefore, this study aims to construct an explainable machine learning-based wildfire susceptibility model and analyze in detail the influence of each factor on the wildfire susceptibility prediction results. Based on historical wildfire samples from Nanning, considering the spatial distribution characteristics of the samples, 18 evaluation factors including elevation, normalized difference vegetation index (NDVI), annual precipitation, and average temperature were selected. Four machine learning models, namely classification and regression tree (CART), random forest (RF), light gradient boosting machine (LGBM), and extreme gradient boosting (XGBoost), were employed to construct wildfire susceptibility prediction models. Based on the best-performing susceptibility model, the SHAP (shapley additive explanations) interpretable method was applied to achieve global feature explanations, dependency analysis, and local analysis of typical samples. The results showed that the XGBoost outperformed other models in terms of predictive performance, and the extremely high susceptibility zones were located in the northwest, east, and south of Nanning, accounting for 39.113% of the total area. Wildfire susceptibility was mainly influenced by nine factors, including NDVI, annual precipitation, and soil type. The local interpretability results for typical historical wildfire samples can provide targeted references and guidance for wildfire disaster management in specific regions of Nanning.