Forest Fire Detection Using Machine Learning: An Intelligent Approach to Real-Time Environmental Monitoring

Forest fires cause severe damage to ecosystems, wildlife, human settlements, and global climate stability. Traditional fire detection methods rely primarily on human surveillance, lookout towers, and limited sensor networks, which suffer from slow response times, limited coverage, high false alarm rates, and difficulty in accessing remote forest areas. This study presents a machine learning-based forest fire detection system that enables quick, accurate, and real-time fire detection through data-driven analysis. The proposed system incorporates data preprocessing, feature extraction , model training, real-time monitoring, and alert generation. A web-based platform with three user modules (admin, forest officer, user) is developed using Django, MySQL, HTML, CSS, and Bootstrap. The findings indicate that machine learning approaches significantly improve detection speed and accuracy,enable faster response from forest authorities, and minimize environmental and wildlife damage.