Forest fires, typically uncontrolled by humans and stemming from natural or accidental causes, significantly damage wildlife and natural resources. In 2015, approximately 3% of the global forest area was affected by fires, with India facing the vulnerability of over 36% of its forest cover to frequent fires. The increasing frequency of forest fires is attributed to human activities, climate change, and other contributing factors. However, traditional detection methods have proven inefficient in keeping pace with this escalation. Recent advancements in machine learning, computer vision, and remote sensing technologies offer promising avenues for identifying and monitoring forest fires. Nonetheless, a precise and coordinated prevention, early warning, and response system is imperative to effectively mitigate these fires’ impacts. Various factors influence fire behavior, including weather conditions, fuel moisture, climate change patterns, lightning occurrences, and vegetation types. Understanding these factors is essential for accurate fire prediction and effective response strategies. Modern technologies such as geospatial data analysis, satellite imagery interpretation, remote sensing techniques, Geographic Information Systems (GIS), Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) play a pivotal role in comprehending fire behavior dynamics. The integration of these technologies has revolutionized wildfire management by enhancing prediction accuracy and response timeliness. Furthermore, utilizing remote sensing and GIS in disaster management encompasses mitigation, preparedness, response, and recovery phases. Looking ahead, there is a growing need to develop integrated, multi-hazard early warning systems that prioritize community needs and encompass the entire disaster management cycle. Investment in research and development is essential to address technological limitations and adapt to the evolving environmental landscape.