This study compares machine learning algorithms for predicting vegetation fire detections in Pakistan. Vegetation fires include forest fires, cropland fires, and other vegetation fires. The research used data from the MODIS Global Fire Atlas, topographic, climatic conditions, and vegetation types from 2001 to 2022. Four models—logistic regression, random forest, support vector machine, and extreme gradient boosting—were tested. The random forest model achieved the highest accuracy and AUC values, making it the most optimal for predicting vegetation fires. The models provided predictive insights into fire occurrences, adding value beyond MODIS detection data. The study mapped vegetation fire risk in Pakistan, highlighting high, moderate, and low risk areas. The results showed that the random forest model had the highest accuracy for forest fires (87.5%), crop fires (84.0%), and other vegetation fires (83.1%). The study also identified key factors influencing fire occurrences, such as soil temperature, minimum temperature, wind components, precipitation, and topographic features. The findings emphasize the importance of using advanced machine learning models for accurate fire prediction and management. The study concludes that the random forest model is the most efficient for vegetation fire prediction in Pakistan, with significant implications for wildfire management policies and strategies.This study compares machine learning algorithms for predicting vegetation fire detections in Pakistan. Vegetation fires include forest fires, cropland fires, and other vegetation fires. The research used data from the MODIS Global Fire Atlas, topographic, climatic conditions, and vegetation types from 2001 to 2022. Four models—logistic regression, random forest, support vector machine, and extreme gradient boosting—were tested. The random forest model achieved the highest accuracy and AUC values, making it the most optimal for predicting vegetation fires. The models provided predictive insights into fire occurrences, adding value beyond MODIS detection data. The study mapped vegetation fire risk in Pakistan, highlighting high, moderate, and low risk areas. The results showed that the random forest model had the highest accuracy for forest fires (87.5%), crop fires (84.0%), and other vegetation fires (83.1%). The study also identified key factors influencing fire occurrences, such as soil temperature, minimum temperature, wind components, precipitation, and topographic features. The findings emphasize the importance of using advanced machine learning models for accurate fire prediction and management. The study concludes that the random forest model is the most efficient for vegetation fire prediction in Pakistan, with significant implications for wildfire management policies and strategies.