Tube-fin heat exchangers (TFHXs) are omnipresent within the air-conditioning and refrigeration industry. Computationally expensive, physics-based models are conventionally used to conduct performance simulations, optimization, and design selection of such devices. In this thesis, a comparative evaluation of machine learning based regression techniques to predict the heat transfer and refrigerant pressure drop of TFHXs for different applications is conducted. Ridge Regression, Support Vector Regression (SVR) and Artificial Neural Network (ANN) models are trained and analysed. Results show that the baseline full-domain SVR and ANN models predict more than 90% of the test dataset within a 20% error band for 5 out of 6 application cases. Subsequently, an outcome-based comparison framework is proposed to understand the cost incurred by an ML model in achieving a predetermined degree of accuracy. As a result, reduced-domain ANN and SVR models with training times that are 2 to 3 orders of magnitude lower than baseline models with little to no degradation in prediction accuracy are obtained. The trained ML models facilitate rapid exploration of the design space with significant reduction in engineering time to arrive at near optimal designs.