Globally, forest fires cause economic losses worth billions of US dollars and contribute significantly to greenhouse gas emissions. Consequently, countries allocate substantial financial resources annually in fire management with the bulk of the budget spent on fire detection and suppression. While the traditional fire management practices are successful to some extent in preventing and controlling the forest fires, they are severely limited by availability of resources and funds, especially in economically weaker countries. There is a greater need to employ innovative techniques and technological advancements in forest fire management. Recent advances in remote sensing and computational abilities have emerged as viable, low-cost methods to supplement traditional fire management. When combined with artificial intelligence (AI), machine learning (ML) and deep learning (DL) techniques, these methods have shown promising results in near-real-time detection of fire incidence, risk zoning and fire predictions. However, the adoption of AI-ML in forest fire management is still in the nascent stages in the global south. In this chapter, we present a review of the recent applications of AI-ML in forest fire management, specifically fire detection, susceptibility mapping, and prediction. We contextualize global methodologies and experiences in spatial prediction of regional forest fire susceptibility, with a case study from India. We further discuss the performance of an AI-ML based model, we developed for predicting and classifying forest fire duration and size in an Indian Himalayan State—Uttarakhand, where increasing instances of forest fires have been reported to increase in frequency and intensity in recent years. We end the chapter by providing a road map and recommendations.