An in-depth analysis of UAV path planning, including procedures, algorithms, optimization models, and emerging challenges

• Comprehensive Overview – The paper surveys UAV path planning research, covering classification techniques, traditional, heuristic / metaheuristic, hybrid, and AI-driven algorithms, along with various problem models. • Key Insights – It evaluates the strengths and weaknesses of existing methods, highlighting their effectiveness in ensuring safe, energy-efficient, and adaptive UAV navigation in complex environments. • Future Directions – The study identifies research gaps and emphasizes challenges such as real-time processing, multi-UAV coordination, energy efficiency, and environmental concerns to guide future innovations. Unmanned aerial vehicles (UAVs) have emerged as valuable assets in modern surveillance, environmental monitoring, disaster response, and delivery systems. Their autonomy is built around effective path planning, which provides safe, energy-efficient, and goal-oriented navigation in complex, dynamic environments. This paper provides a comprehensive overview of UAV path planning research, encompassing classification techniques, heuristic and metaheuristic algorithms, and underlying problem models. It emphasizes significant contributions from traditional, hybrid, and AI-driven techniques while carefully highlighting upcoming issues, such as real-time processing, multi-UAV coordination, energy limitations, and environmental concerns. The study provides a detailed assessment of the strengths and weaknesses of significant algorithms, allowing for the identification of research gaps and future initiatives. This paper is an invaluable resource for researchers and practitioners seeking to create robust, scalable, and adaptive UAV route planning systems appropriate for a wide range of mission-specific and real-world applications.