Machine learning (ML) is a critical tool in data science, enabling robust predictive modeling across diverse domains. Selecting the optimal algorithm for classification tasks is challenging due to the variability in data and problem complexity. This paper compares four popular ML algorithms—Decision Trees, Random Forests, Support Vector Machines (SVM), and Neural Networks—on classification tasks using the Iris, Wine, and Breast Cancer datasets from the UCI Machine Learning Repository. Performance is evaluated using accuracy, precision, recall, F1-score, and computational efficiency metrics. Results show that Neural Networks achieve the highest accuracy and F1 scores, excelling with complex, non-linear relationships but requiring substantial computational resources. SVMs perform well on datasets with clear class separations but are less interpretable. Random Forests balance accuracy and efficiency, particularly for large datasets with critical feature importance. While less accurate, decision trees are highly interpretable and computationally efficient, making them suitable for more straightforward tasks. This study highlights the importance of algorithm selection tailored to application-specific requirements, providing practical insights for enhancing predictive performance.