Machine learning algorithms play a pivotal role in classification tasks where selecting the suitable method is crucial for achieving optimal performance. Ensemble methods such as Random Forest, Rule Induction, and Overbagging boost predictive accuracy by integrating multiple models. This research reviews the efficiency of Random Forest, Rule Induction, and Overbagging for classification tasks in healthcare, education, and fraud detection, with a focus on feature dimensionality, interpretability, and class imbalance. By utilizing diverse datasets, we evaluate the performance of each model in terms of accuracy, precision, recall, and F1 score. Our observations accentuate the advantages and drawbacks of each model which provides significant insights on algorithm choice for real-world applications.