This paper explores the application of metaheuristic optimization methods in the context of Multiple Sclerosis (MS) detection, addressing the challenges of early and accurate diagnosis. It introduces Genetic Algorithms, Simulated Annealing, and Particle Swarm Optimization as effective tools for feature selection and classification in MS detection. Additionally, the paper discusses emerging metaheuristic optimization methods and their potential contributions. Through critical evaluation, it emphasizes the need for method selection based on task-specific requirements. In conclusion, this research highlights the promise of metaheuristic optimization in revolutionizing MS detection, urging further development and validation on larger datasets. Future directions include hybrid algorithms and advanced machine learning integration for improved diagnostic precision.