Forest fires in Thailand are a recurring and formidable challenge, inflicting widespread damage and ranking among the nation's most devastating natural disasters. Most detection methods are labor-intensive, lack speed for early detection, or result in high infrastructure costs. An essential approach to mitigating this issue involves establishing an efficient forest fire warning system based on amalgamating diverse available data sources and optimized algorithms. This research endeavors to develop a binary machine-learning classifier based on Thailand's forest fire occurrences from January 2019 to October 2022 using data acquired from satellite resources, including the Google Earth engine. We use four gas variables including carbon monoxide, sulfur dioxide, nitrogen dioxide, and ozone. The study explores a range of classification models, encompassing linear classifiers, gradient-boosting classifiers, and artificial neural networks. The XGBoost model is the top-performing option across various classification evaluation metrics. The model provides the accuracy of 99.6 % and ROC-AUC score of 0.939. These findings underscore the necessity for a comprehensive forest fire warning system that integrates gas measurement sensor devices and geospatial data. A feedback mechanism is also imperative to enable model retraining post-deployment, thereby diminishing reliance on geospatial attributes. Moreover, given that decision-tree-based algorithms consistently yield superior results, future research in machine learning for forest fire prediction should prioritize these approaches.