This study presents a comprehensive comparative analysis of machine learning algorithms and dimensionality reduction techniques for classifying myoelectric hand gestures, aimed at improving prosthetic control. The analysis focuses on five distinct finger movements using SEMG data from an eight-channel system. Four popular algorithms—Decision Tree, K-Nearest Neighbors (KNN), Multilayer Perceptron (MLP), and Support Vector Machine—were compared. Additionally, the impact of feature dimensionality was analyzed by comparing two reduction methods, Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE), limiting usage to four electrodes. Key findings reveal that KNN achieved the highest classification accuracy (95%) when combined with PCA, demonstrating robustness across different experimental setups. Decision Tree emerged as the most computationally efficient, making it ideal for real-time applications. SVM and MLP, while achieving competitive accuracy rates (92% and 93%, respectively), exhibited limitations in computational efficiency, particularly in training times. The analysis also emphasizes the critical role of electrode configurations, showing that optimized feature selection and electrode placement can significantly enhance classification performance. In conclusion, the comparative analysis underscores the competitive performance of our model across diverse machine learning algorithms and feature extraction techniques, positioning it as a viable and versatile solution for accurate gesture recognition, with performance metrics rivaling or surpassing those documented in the existing literature.