Autonomous unmanned aerial vehicles (UAVs) are regarded as systems with great potential for various industrial and military applications. This paper addresses the fundamental technologies and algorithms used in the path planning and obstacle detection processes of UAVs in three-dimensional environments. The contributions of advanced techniques such as artificial neural networks (ANNs), stereo imaging, sensor fusion, and optical flow to environmental sensing and decision-making processes in autonomous UAV systems are examined. Additionally, the importance of A* and RRT* algorithms in ensuring safe and efficient path planning in complex environments is highlighted. By reviewing the existing literature, this paper comprehensively discusses the opportunities, challenges, and future development areas of these technologies. Critical challenges such as energy efficiency, collision avoidance, and multi-obstacle detection are also discussed, with a focus on the need for further advancements in algorithms to overcome these issues. In conclusion, the paper emphasizes that autonomous UAV technologies provide a strong foundation for future research and applications.