The increasing frequency of forest fires requires the development of advanced detection and monitoring systems to mitigate their devastating environmental, economic, and social impacts. This paper presents the Forest Fire Monitoring Using Machine Learning (FORML) model, an innovative approach that integrates Wireless Sensor Networks (WSNs) and Unmanned Aerial Vehicles (UAVs) for real-time forest fire detection and monitoring. Ground sensors continuously collect environmental data, including temperature, humidity, wind speed, and CO2 levels, while UAVs capture high-resolution visual and thermal imagery. These multimodal datasets are processed using advanced machine learning algorithms to accurately detect fire outbreaks, predict their spread, and generate timely alerts. The proposed model demonstrates superior performance in detection accuracy, false alarm reduction, and adaptability to various environmental conditions. Simulation-based evaluations achieved a detection accuracy of 94.4%, with the system capable of generating fire alerts in 10 s. By combining advanced data fusion techniques and predictive modeling, FORML offers a robust and energy-efficient solution for forest fire monitoring, significantly improving response times and ensuring comprehensive disaster management.