Wildfires pose escalating environmental, social, and economic challenges worldwide, intensified by climate change, prolonged droughts, and expanding human activity. This study reviews the evolution of wildfire management from traditional detection, control, and prevention methods to advanced, data-driven approaches. It highlights the limitations of historical practices, such as reliance on ground-based monitoring, satellite imaging, and fire weather indices, and explores current global and regional strategies, including the integration of remote sensing, advanced analytics, and predictive modelling. Special attention is given to the role of machine learning (ML), particularly ensemble methods such as Random Forest (RF) and XGBoost, which have demonstrated superior predictive performance by capturing complex, non-linear relationships in wildfire data. The study synthesizes key data sources, including meteorological, vegetation, land-type, and satellite datasets, and outlines methods for their integration to improve predictive accuracy. Despite significant progress, persistent challenges remain in data fusion, spatial-temporal modelling, model generalizability, and interpretability. Future research emphasizes multi-source data integration, advanced feature engineering, spatio-temporal analysis, and enhanced model explainability using SHAP and LIME to ensure transparent and actionable predictions. Ultimately, this review underscores the potential of ML-driven systems to transform wildfire prediction into a proactive and sustainable component of modern disaster management.
Said et al. (Thu,) studied this question.
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