<p>Under the dual pressures of climate change and human activities, the frequency and intensity of global wildfires have significantly increased. While seasonal differences profoundly affect the intensity and spatial patterns of wildfire driving factors, past research has largely focused on annual scales, with insufficient attention paid to the dynamic changes and deeper impacts of driving factors in the seasonal dimension. Taking seasonal variations as the core entry point, this study integrated cross-border resources in the Sino-Mongolian border area, adopted satellite fire point data from 2001 to 2022, fused multi-source data including meteorological, topographic, vegetation, socioeconomic and anthropogenic activity data, incorporated meteorological data under three future climate scenarios, and compared the applicability of six models (Logistic Regression (LR), Gompit Regression (GR), Random Forest (RF), Boosted Regression Trees (BRT), XGBoost, and Support Vector Machine (SVM)) in wildfire prediction on the Mongolian Plateau. The results indicate that the Boosted Regression Trees model is the optimal model. Daily average relative humidity (Hum) and yearly average wind speed (Y_win) are the primary driving factors. The eastern provinces of Mongolia, Khovd Province, Selenge Province, and Hulunbuir City in China are identified as extremely high-risk areas for wildfires, with an increasing trend in wildfire incidents on the Mongolian Plateau in the future. This study improves the analysis of fire risk level zoning to accurately identify the spatial characteristics of high-risk areas and clarifies critical thresholds through the marginal benefit analysis of driving factors. Based on this, differentiated early warning systems can be initiated in conjunction with the specific conditions of high-risk areas, supported by targeted prevention and control measures, enhancing the foresight and effectiveness of wildfire risk management in cross-border regions.</p>