Artificial intelligence (AI) technology has significantly contributed to innovation in manufacturing, particularly in improving quality management processes. One key area within AI is machine learning (ML), which has developed into a distinct field, with ensemble learning gaining increasing attention. This paper explores the concept of ensemble learning and its application in quality management, with a specific focus on quality control and quality assurance. We assess the performance of three widely used tree-based algorithms: Random Forest, Extreme Gradient Boosting (XGBoost), and Adaptive Boosting (AdaBoost). Through a detailed evaluation process, we review models frequently utilized in prior research to highlight their distinctive advantages. Our findings indicate that Random Forest is the most prevalent algorithm, demonstrating superior performance over not only basic ML algorithms but also deep learning models. Its success is largely due to its simplicity, scalability, and robust ability to handle multidimensional data.