<p>Wildfires significantly disrupt the physical and hydrologic conditions of the environment, leading to vegetation loss and altered surface geo-material properties. These complex dynamics promote post-fire gully erosion, yet the key conditioning factors (e.g., topography, hydrology) remain insufficiently understood. This study proposes a novel artificial intelligence (AI) framework that integrates four machine learning (ML) models with Shapley Additive Explanations (SHAP) method, offering a hierarchical perspective from global to local on the dominant factors controlling gully distribution in wildfire-affected areas. In a case study of Xiangjiao catchment burned on March 28, 2020, in Muli County in Sichuan Province of Southwest China, we derived 21 geoenvironmental factors to assess the susceptibility of post-fire gully erosion using logistic regression (LR), support vector machine (SVM), random forest (RF), and convolutional neural network (CNN) models. SHAP-based model interpretation revealed eight key conditioning factors: topographic position index (TPI), topographic wetness index (TWI), distance to stream, mean annual precipitation, differenced normalized burn ratio (dNBR), land use/cover, soil type, and distance to road. Comparative model evaluation demonstrated that reduced-variable models incorporating these dominant factors achieved accuracy comparable to that of the initial-variable models, with AUC values exceeding 0.868 across all ML algorithms. These findings provide critical insights into gully erosion behavior in wildfire-affected areas, supporting the decision-making process behind environmental management and hazard mitigation.</p>