The paper describes the training, validation, testing, and application of models of artificial neural networks (ANN) for computing the cross-shore beach profile of the sand beaches of the province of Valencia (Spain). Sixty ANN models were generated by modifying both the input variables as the number of neurons in the hidden layer. The input variables consist of wave data and sedimentological data. To select and evaluate the performance of the optimal model, the following parameters were used: R², absolute error, mean absolute percentage error, and percentage relative error. Finally, the results are compared with the numerical model proposed by Aragonés et al. ( 2016b Aragonés, L. , Y. Villacampa, F. J. Navarro-González, and I. López. 2016b. Numerical modelling of the equilibrium profile in Valencia (Spain). Ocean Engineering, 123:164–73. doi:10.1016/j.oceaneng.2016.07.036 [Crossref], [Web of Science ®] , [Google Scholar] ) for the equilibrium profile in the study area. The results show a mean absolute error of 0.21 m compared to 0.33 m Aragones’ model, significantly improving the results of the numerical model in the bar area around de Valencia Port. In addition, when comparing the results with other methods currently used (Dean’s or Vellinga formulation), the errors of these compared to ANN are of the order of 167 and 1538% higher, respectively.