This paper provides a comprehensive review of metaheuristic algorithms, which are optimization techniques designed to find adequate solutions for a wide range of optimization problems. Metaheuristics are derivative-free, stochastic, and flexible, making them effective for handling complex and nonlinear optimization tasks. The paper discusses the key components and concepts of various types of metaheuristic algorithms, including evolution-based, swarm intelligence-based, physics-based, human-related, and hybrid methods. It highlights their benefits and limitations, and provides practical applications in fields such as electrical engineering, industrial scheduling, civil engineering, communication, and data mining. The paper also addresses challenges associated with metaheuristic algorithms and suggests future research directions, emphasizing the need for more rigorous theoretical analysis and practical applications.This paper provides a comprehensive review of metaheuristic algorithms, which are optimization techniques designed to find adequate solutions for a wide range of optimization problems. Metaheuristics are derivative-free, stochastic, and flexible, making them effective for handling complex and nonlinear optimization tasks. The paper discusses the key components and concepts of various types of metaheuristic algorithms, including evolution-based, swarm intelligence-based, physics-based, human-related, and hybrid methods. It highlights their benefits and limitations, and provides practical applications in fields such as electrical engineering, industrial scheduling, civil engineering, communication, and data mining. The paper also addresses challenges associated with metaheuristic algorithms and suggests future research directions, emphasizing the need for more rigorous theoretical analysis and practical applications.