An alternative model using support vector regression (SVR) based on dynamically optimized search technique with k-fold cross-validation, was proposed to predict the thermal-hydraulic performance of compact heat exchangers (CHEs). 48 experimental data points from the author's own study were used in the present work. The performance of SVR with different regularization parameter gamma and kernel parameter sigma(2) had been investigated and the optimal values were obtained. According to predicted accuracy of indicating generalization capability, the model performance was compared and evaluated with the artificial neural network (ANN) model. As a result, it is found that, the SVR provides better prediction performances with the mean squared errors (MSE) of 2.645 x 10(-4) for testing j factor and 1.231 x 10(-3) for testing f factor, respectively. Also the computational time of SVR model was shorter than that of the ANN model. Moreover, the versatility of the configured SVR model was demonstrated by presenting the effects of some input variables on the output variables. The result indicated that SVR can offer an alternative and powerful approach to predict the thermal characteristics of new type fins in CHEs under various operating conditions. (C) 2015 Elsevier Ltd. All rights reserved.