The Job Shop Scheduling Problem (JSSP) is a classic NP-hard optimization problem encountered in manufacturing and industrial processes. It involves scheduling a set of jobs, each with a sequence of operations, onto a finite number of machines while optimizing specific objectives such as minimizing makespan or total tardiness. Given the computational complexity of JSSP, metaheuristic algorithms have emerged as powerful tools for finding near-optimal solutions. This paper provides a comparative analysis of several prominent metaheuristic algorithms, including Genetic Algorithms (GA), Simulated Annealing (SA), Tabu Search (TS), Ant Colony Optimization (ACO), and Particle Swarm Optimization (PSO). The study evaluates these algorithms based on performance criteria such as runtime, convergence, and scalability. The findings highlight the strengths and limitations of each approach, providing insights into their suitability for different JSSP instances. Furthermore, the paper discusses recent hybrid metaheuristic approaches and outlines future research directions for improving JSSP optimization.