Forest fires account for 3–5% of world greenhouse gas emissions, leading to major environmental and economic losses. Precise forecasting is critical to allow timely intervention and disaster management. The present paper proposes a forest fire forecasting system combining deep learning models with explainable AI methods to improve the accuracy and transparency of forecasts. Based on past meteorological and fire data, the system predicts region-wise fire hazards through data preprocessing and clustering. The models are tested using evaluation measures, while explainable AI methods reveal important environmental variables determining fire events. The method enhances fire hazard prediction for efficient management of wildfires.