This paper introduces an EEG signal classification approach, leveraging machine learning algorithms. The methodology involves the extraction of features from EEG signal datasets through discrete wavelet transform (DWT). Optimal feature selection is then accomplished using evolutionary algorithms, specifically Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). To identify the most effective classification method, various machine learning algorithms, including Support Vector Machines (SVM), Naive Bayes, Decision Trees, and Random Forest, are systematically compared. This comprehensive evaluation aims to enhance the accuracy and efficiency of EEG signal classification for improved diagnosis and understanding of neurological conditions.