International Journal of Advances in Applied Computational Intelligence IJAACI 2833-5600 10.54216/IJAACI https://www.americaspg.com/journals/show/2439 2022 2022 Digital Charging Solutions GmbH, Germany Mustafa El .. University of Technology and Applied Science, Shinas, Oman Aaras Y. Kraidi 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 2023 33 40 10.54216/IJAACI.040204 https://www.americaspg.com/articleinfo/31/show/2439