This study presents an artificial neural network approach in combination with numerical methods to calculate the heat transfer area assuming a nonlinear variation of the global heat transfer coefficient as a consequence of the thermophysical properties of the fluids, the geometry of the surfaces, and other factors. The development of the article is presented in two applications. The first application takes up the database described by Allan P. Colburn, four possibilities are proposed using functions from the field of artificial neural networks to create several approaches. The second application is presented to verify the goodness of the proposed methodology, the artificial neural network model is applied in an experimental data set of double‐pipe vertical heat exchangers, the comparison between the calculated and experimental heat transfer area shows a relative percentage error smaller than 2.8%. The results in the applications are evidence of the competitiveness of the artificial neural network for the prediction of the heat transfer area considering a variable overall heat transfer coefficient.