South Korea, with approximately 63% of its land covered by forests, is highly susceptible to wildfires. Traditional fire detection methods-such as satellite imagery and ground-based observation-face significant limitations, including high operational costs, delayed response times, and vulnerability to weather conditions. This paper presents an efficient fire detection system for Vertical Take-Off and Landing (VTOL) Unmanned Aerial Vehicles (UAVs), utilizing Convolutional Neural Networks (CNNs). The integration of CNNs significantly improves detection accuracy, even in complex environments that challenge conventional approaches. In simulations designed to closely mimic real-world scenarios, the optimized algorithm achieved a 93% detection rate with 20% false positives and a frame latency of just 1.2 seconds. Additionally, deploying the model on a Raspberry Pi onboard a VTOL drone demonstrated its practical viability for real-time forest fire surveillance and rapid response. This study highlights the potential of drone-based, AI-powered fire detection systems as a powerful supplement to existing wildfire monitoring and prevention strategies.