Heat exchangers are critical components widely used in various industries such as chemical processing, automotive, and HVAC. The evaluation and optimization of heat exchanger design criteria play a vital role in improving industrial applications. Tree-based machine learning models offer a powerful alternative to time-consuming numerical solutions by enabling optimization and classification predictions for problems involving small, medium, or large datasets. This study aims to analyze heat exchanger design criteria using tree-based machine learning models and to identify the most suitable model for each design parameter. As a result, it has been evaluated that the XGBoost model provides effective solutions for design criteria such as heat transfer rate, safety, and reliability; the AdaBoost model is more suitable for criteria such as exchanger type and ease of maintenance; and the RF model performs well for cost and pumping power. It is anticipated that in the future, analyzing heat exchanger design parameters using various machine learning approaches will enable the development of more cost-effective and efficient heat exchangers.