This paper presents a comprehensive review of unmanned aerial vehicle (UAV) path planning, categorizing algorithms at both algorithmic and functional levels. It discusses the advantages, disadvantages, and challenges of various path-planning algorithms, aiming to provide a thorough understanding of their performance. The paper also explores future research directions for UAV path planning. UAVs are widely used in various industries due to their flexibility, mobility, and cost-effectiveness. Path planning is critical for ensuring safe and efficient flight, considering factors such as flight capabilities, path security, and mission reliability. The complexity of path planning is further increased by real-world constraints such as connectivity, fuel limitations, and collisions. The paper classifies path-planning algorithms into three categories at the algorithmic level: traditional, intelligent, and hybrid. At the functional level, algorithms are classified into space-based, time-based, and task-based planning. The shortest path problem, traveling salesman problem (TSP), and area coverage problem are introduced. Traditional algorithms include cell-based, model-based, graph-based, and potential field methods. Intelligent algorithms include swarm intelligence, artificial intelligence (AI), and hybrid algorithms. The paper discusses various algorithms, their performance, and challenges. The paper also explores the application of UAV communication networks (UAVCNs) in path planning, particularly in disaster management. It highlights the importance of path planning in UAV operations and the challenges associated with it. The paper concludes with future research directions for UAV path planning, emphasizing the need for further exploration and innovation in this field.This paper presents a comprehensive review of unmanned aerial vehicle (UAV) path planning, categorizing algorithms at both algorithmic and functional levels. It discusses the advantages, disadvantages, and challenges of various path-planning algorithms, aiming to provide a thorough understanding of their performance. The paper also explores future research directions for UAV path planning. UAVs are widely used in various industries due to their flexibility, mobility, and cost-effectiveness. Path planning is critical for ensuring safe and efficient flight, considering factors such as flight capabilities, path security, and mission reliability. The complexity of path planning is further increased by real-world constraints such as connectivity, fuel limitations, and collisions. The paper classifies path-planning algorithms into three categories at the algorithmic level: traditional, intelligent, and hybrid. At the functional level, algorithms are classified into space-based, time-based, and task-based planning. The shortest path problem, traveling salesman problem (TSP), and area coverage problem are introduced. Traditional algorithms include cell-based, model-based, graph-based, and potential field methods. Intelligent algorithms include swarm intelligence, artificial intelligence (AI), and hybrid algorithms. The paper discusses various algorithms, their performance, and challenges. The paper also explores the application of UAV communication networks (UAVCNs) in path planning, particularly in disaster management. It highlights the importance of path planning in UAV operations and the challenges associated with it. The paper concludes with future research directions for UAV path planning, emphasizing the need for further exploration and innovation in this field.