This paper presents an in-depth analysis of UAV path-planning algorithms, categorizing them into traditional, intelligent, and hybrid classes at the algorithmic level, and space-based, time-based, and task-based planning at the functional level. The authors discuss the advantages, disadvantages, and challenges of each algorithm, providing a comprehensive reference for researchers. The paper also explores the evolution and current paradigms in UAV path-planning methodologies, aiming to identify research gaps and future opportunities. Key contributions include a detailed analysis of various path-planning algorithms, a systematic classification, and insights into future research directions. The paper highlights the importance of integrating AI and multiple planning algorithms to enhance performance and adaptability in complex environments.This paper presents an in-depth analysis of UAV path-planning algorithms, categorizing them into traditional, intelligent, and hybrid classes at the algorithmic level, and space-based, time-based, and task-based planning at the functional level. The authors discuss the advantages, disadvantages, and challenges of each algorithm, providing a comprehensive reference for researchers. The paper also explores the evolution and current paradigms in UAV path-planning methodologies, aiming to identify research gaps and future opportunities. Key contributions include a detailed analysis of various path-planning algorithms, a systematic classification, and insights into future research directions. The paper highlights the importance of integrating AI and multiple planning algorithms to enhance performance and adaptability in complex environments.