Climate change continues to worsen the frequency of forest fires and other natural disasters around the globe. Intense and recurring extreme weather conditions, particularly in sensitive areas such as the Mediterranean which is susceptible to intense and frequent fires, amplify the frequency and severity of forest fires. This study applies Machine Learning (ML), Remote Sensing, and Geographic Information Systems (GIS) to predict forest fire risk using the XGBoost algorithm. The model incorporates several factors including topographic features (slope, aspect, and elevation), meteorological features (relative humidity, temperature, dew point, wind speed, precipitation), anthropogenic influences (distance from urban centers, roads, and cultivated land), and the types of fuel available. The final output, the Forest Fire Risk Map, divides the region into three risk zones: High, Moderate, and Low to enhance community and stakeholder participation and to advance preparedness measures in emergencies. The forecasting model showed remarkable predictive capabilities, achieving 0.94 accuracy and 0.99 AUC, which emphasizes its ability to improve fire management systems and response strategies, along with disaster response readiness.