A Review of Classification and Application of Machine Learning in Drone Technology
Ahshanul Haque, Md. Tanvir Chowdhury, Mostafa Hassanalian · AI Computer Science and Robotics Technology · 2025
The integration of drones in various applications has seen rapid growth in recent years, with machine learning emerging as a critical enabler of this advancement. This study provides a comprehensive survey of the classification and applications of machine learning techniques in drone technology. By reviewing key machine learning paradigms—supervised, unsupervised, and reinforcement learning—and their relevance to drones, this survey highlights real-world applications, including object recognition, route planning, obstacle avoidance, search area optimization, and autonomous navigation. Furthermore, it examines the challenges associated with implementing machine learning in drones, such as data privacy concerns, data quality issues, and computational limitations. By exploring future directions and the transformative potential of machine learning in enhancing drone capabilities across industries, this survey aims to serve as a valuable resource for researchers, practitioners, and students exploring the intersection of machine learning and unmanned aerial vehicle technology.