Forest fires pose a significant threat to ecosystems, wildlife, and human settlements, often causing catastrophic damage. Early detection and rapid response are crucial for mitigating these risks. This paper presents an innovative approach to forest fire prediction and suppression using ultraviolet (UV) radiation sensors, environmental monitoring systems, and an automatic water spray mechanism. UV radiation emitted by fires is detectable in the early stages, long before visible smoke or heat becomes apparent. By deploying UV sensors, along with complementary temperature, humidity, and smoke detectors, the system continuously monitors the forest environment. Data collected from these sensors is processed using machine learning models to predict fire risks based on real-time conditions. Upon detection of a fire threat, an automatic water spray system is triggered, either via ground-based sprinklers, drones, or autonomous robots, to quickly suppress The potential fire. This system provides proactive protection by not only predicting fire outbreaks but also activating fire suppression measures immediately. The integration of these technologies promises to enhance forest fire management by improving prediction accuracy, minimizing response time, and reducing the overall environmental impact of wildfires. Challenges, such as sensor calibration, false positives/negatives, and power supply considerations, are addressed, and potential real-world applications in fire-prone regions are explored.