aspg-31-2439-1791686726 20261011024526 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group International Journal of Advances in Applied Computational Intelligence IJAACI 2833-5600 2023 4 2 Mustafa El-Taie Digital Charging Solutions GmbH, Germany Aaras Y. Kraidi University of Technology and Applied Science, Shinas, Oman Unmanned aerial vehicles (UAVs) equipped with high-altitude infrared imaging have revolutionized data collection, providing better spatial and temperature resolutions. However, an effective way to fuse and interpret this multidimensional data remains a challenge. Therefore, this research tackles this issue by incorporating machine learning specifically the YOLO object detector to fuse and analyze information from UAV-captured high-altitude infrared images. The process entails a careful fusion of data, feature extraction, and model configuration that is tailored to the unique qualities of infrared imagery. Furthermore, the confabulated YOLO model performs exceptionally well in detecting and localizing objects within the thermal spectrum. Results showed precise identification of objects as well as their localization thus indicating potential for advanced aerial surveillance and monitoring. This research represents a significant advancement in situation awareness across environmental monitoring, infrastructure inspection, and disaster response among other areas hence demonstrating the transformative ability of machine learning in aerial imaging analysis. 2023 33 40 2439 https://creativecommons.org/licenses/by/4.0/ 10.54216/IJAACI.040204 https://www.americaspg.com/journal/31/article/2439 https://www.americaspg.com/storage/81705854775.pdf