Forest fires are becoming an increasing threat to flora and fauna due to anthropogenic activities contributing to global warming and accelerating climate change. These changes significantly influence forest fires’ frequency, magnitude, and severity, leading to increased fire risks, longer fire seasons, and more severe fire consequences. To address these challenges, various climate models such as GCM (General Circulation Models), MaxEnt (Maximum Entropy), MMM (Multi-Model Mean) techniques, CNN (Convolutional Neural Networks), BPNN (Backpropagation Neural Networks), LSTM (Long Short-Term Memory), SVM (Support Vector Machines), and ARIMA (AutoRegressive Integrated Moving Average) are utilized to predict and forecast the future behavior of forest fires. Monitoring forest fires’ occurrence, behavior, severity, and spread is crucial for effective prevention and mitigation. Reducing uncertainty in baseline data through enhanced monitoring techniques, creating precautionary measures, integrating institutional efforts, implementing legal actions, and funding additional programs are essential steps to improve the current situation. These mitigation measures and methods will contribute to a healthier ecological cycle and more resilient forest ecosystems.