In Indonesia, wildfires have become an annual disaster that results in significant losses across various aspects of life, including ecological, social, and economic conditions. To minimize these losses, accurate wildfire predictions are urgently needed for prevention, early detection, and wildfire management decision support. This study employs an ensemble learning approach to develop a prediction model for wildfire occurrences and create a susceptibility map of fire-prone areas on a national scale in Indonesia. The proposed model is Stacking Ensemble Learning (SEL), which integrates K-Nearest Neighbor (KNN), Adaptive Boosting (AdaBoost), and Extreme Gradient Boosting (XGBoost) as the base learners, with Random Forest (RF) serving as the meta-learner. The final performance results indicate an accuracy 0.985, balanced accuracy 0.97, precision 0.96, average precision 0.896, recall 0.92, F1-Score 0.954, Matthew's Correlation Coefficient (MCC) 0.944, and AUC Score 0.97, with no signs of overfitting and optimal computational efficiency. SHAP Explainable AI analysis is employed to identify the most influential factors, revealing that proximity to residential areas and climate factors are the most significant contributors to wildfire occurrences in Indonesia. The susceptibility mapping results highlight the provinces with the largest and most vulnerable areas: West Kalimantan, South Sumatra, and South Sulawesi. The outcome of this study can assist stakeholders in mitigating wildfires and protecting the environment to achieve sustainable development goals. Copyright © 2026. Publishing services by Elsevier B.V.