Forest fires pose a significant threat to ecosystems, biodiversity and human livelihoods, necessitating the development of advanced predictive models for timely intervention. Machine learning (ML) techniques have emerged as effective tools for predicting forest fires by utilizing various environmental, meteorological and topographical datasets. This review explores recent advancements in ML-based fire prediction, highlighting key methodologies such as Random Forest, Support Vector Machines, Neural Networks and hybrid models. The combination of remote sensing data, real-time observation and cloud computing has notably improved the prediction accuracy. Moreover, the growth of Explainable AI (XAI) has enhanced the interpretability of ML models. Despite advancements, challenges such as data imbalances, model generalization issues and computational constraints persist. Future research must focus on refining ML algorithms to improve regional adaptability, integrating climate change forecasts and establishing real-time early warning systems. By bridging the gap between theoretical advancements and practical applications, ML-driven forest fire prediction models can significantly contribute to mitigating the devastating impact of forest fire and enhancing global fire management strategies.
