Wildfires are some of the most devastating natural disasters with devastating ecological, economic, and infrastructural losses all over the globe. This paper is a proposal of a real-time detection and risk evaluation system of wild fires incorporating the Internet of Things (IoT) and Artificial Intelligence (AI) technologies to detect fires early and respond to them as quickly as possible. IoT-enabled sensor nodes constantly check environmental conditions (temperature, humidity, smoke, gas concentration, and presence of flames) and report them to the system. Multi-sensor data collection is controlled by an ESP32-based controller which carries out edge-based intelligent processing to reduce latency. Hybrid AI-based decision model implements the use of multi-sensor data fusion to minimize false alarm and enhance the level of classification reliability. In case of high fire hazard, a GSM module sends real-time SMS notification and GPS coordinates to the emergency departments, and a cloud-based IoT system provides access to remote monitoring, visualization, and analysis of past data. This is through the provision of an automated water motor activation mechanism that provides immediate initial fire fight to contain the spread of the fire. Experimental findings prove the existence of a 96% detection rate which is 14% higher than compared to conventional systems and the false alarm rate decreases to 4 percent as opposed to 18 %. The proposed edge architecture reduces response time by a factor of 3 and results in an almost 68% faster intervention around 25 seconds to 8 seconds. Uptime of the system was 98 % with communication reliability of 97% and power usage was lowered down to 3.2 W per node instead of 5.8 W per node which also prolonged its operation life. The results of these studies validate the fact that the combined IoT-AI system will provide a scaleable, energy efficient, and highly reliable solution to proactive wildfire prevention and disaster management.