Metaheuristic algorithms have garnered significant attention in the field of optimization due to their ability to address complex, nonlinear, and combinatorial problems where conventional exact methods are often impractical. Inspired by natural phenomena, social behaviors, and physical processes, these algorithms provide near-optimal solutions within reasonable computational time by balancing exploration and exploitation. This paper presents a comprehensive review of metaheuristic algorithms, categorizing them into single-solution-based and population-based approaches. It further discusses hybrid and adaptive variants designed to overcome limitations such as premature convergence and parameter sensitivity. The study highlights the advantages, disadvantages, and practical applications of various metaheuristics across diverse domains including engineering, logistics, artificial intelligence, energy systems, and bioinformatics offering researchers a structured guide for selecting appropriate algorithms based on problem characteristics.