Wildfires represent an escalating global threat to both the environment and society due to their frequency, duration, and expansion, which is exacerbated by climate change. Furthermore, recent heat waves underscore the critical need for swift detection and response mechanisms to curb wildfires from escalating into fully developed and uncontrollable stages. In that sense, this manuscript addresses an aerial smoke detector, composed of an unmanned aerial vehicle equipped with a camera, where visual information is sent to a ground station based on robotic operating system, where the proposed smoke detection methodology is deployed in real time. Such approach is formulated on an optimized YOLOv8 nano model, which is specifically trained using a customized data base with smoke under defying conditions. This solution ensures peak performance even within limited computational resources. The experimentally tests conducted first using images and videos, then, taking the video from the drone under controlled laboratory conditions, and finally by unstructured field experiments, such scenarios determine its robustness under such challenged conditions, producing confidence over 70%, and reducing the bias by validation metrics such as 95% of precision, and 88.5% of recall, respectively.