Wildfires present a severe risk to environmental sustainability, public health, and economic stability, especially in wildfire-prone areas, e.g., northern Thailand. This paper proposes a real-time wildfire-prone area monitoring and early warning system with a detection technique to address these challenges. First, we developed a web application that combines IoT cameras with a YOLOv5-based smoke detection model to monitor, detect, and notify users about potential wildfires. Second, we propose a two-stage smoke detection framework that leverages Gaussian filtering and a dual-stage YOLOv5 pipeline to reduce false predictions and improve detection accuracy. Using the FireSpot dataset comprising annotated images of early-stage wildfires, the system achieved a balanced accuracy of 98.62%, significantly surpassing baseline models with an improvement of 4.02%. Currently deployed in three municipalities in Chiang Mai, Thailand, the system has proven effective in providing timely alerts and enhancing wildfire management. By integrating IoT technology and machine learning, this study offers a scalable and practical solution for early-stage wildfire detection to mitigate environmental and human health impacts.