The study evaluates and compares the performance of various machine learning algorithms for voice recognition and classification tasks. The primary objective is to analyze the accuracy, efficiency, and robustness of algorithms, such as Random Forest, SMO, Logistic Regression, Naïve Bayes, and others, on a voice dataset. Using the Weka tool for model training and testing, key performance metrics, including accuracy, precision, recall, F-measure, and ROC area, were examined. The results indicate that Random Forest outperforms other algorithms with the highest accuracy (97.95%) and robustness in handling voice data. While algorithms like SMO and Logistic Regression also show strong performance, Naïve Bayes exhibits lower accuracy and efficiency, particularly in handling complex voice patterns. The hypothesis testing reveals a statistically significant difference in algorithm performance, emphasizing the importance of selecting the most suitable machine learning technique based on the specific needs of voice recognition applications. This study provides valuable insights for developing more accurate and efficient voice recognition systems.