Every year, forest fires cause catastrophic destruction of houses, properties, lives, and flora and wildlife around the world, destroying thousands of hectares. Wildfires are a major threat to healthy forests and environmental safety. This problem has been studied for a long time, and several approaches have been developed to fix it. In order to better avoid fires, it is important to understand their spatial distribution, even if the bulk of fires originate during the dry season. Effective forest fire prediction models contribute to the prevention of forest fires and their adverse effects. In this study, eleven machine learning (ML) algorithms were evaluated in order to assess the flammability of India’s forests. We reviewed the best machine learning (ML) algorithms for activating a fire prediction model using minimal forest, climate, and topographic parameters, including artificial neural networks (ANN), support vector machine (SVM), multi-variate logistic regression (MLR), random forest classification (RFC), decision tree classification (DTC), K-nearest neighbours (KNN), Bayes network (BN), naive Bayes (NB), linear regression, logistic regression, and fire prediction using fuzzy logic.