In recent years, the use of machine learning methods for predicting the mechanical properties of concrete has received significant attention. Among these, accurate prediction of concrete compressive strength is considered one of the most important design parameters in concrete pavements. However, the limitation of experimental data is one of the main challenges in developing accurate machine learning models. In this study, a hybrid approach based on the Gaussian copula model and artificial neural networks (Copula-ANN) is proposed to augment the training data and improve the accuracy of compressive strength prediction of concrete. The dataset used in this research was extracted from the standard Yeh dataset and includes concrete mix design variables and compressive strength. The performance of the proposed model was compared with a conventional neural network and linear regression. The results showed that the use of the copula method significantly improved the performance of the neural network, increasing the coefficient of determination (R²) from 0.914 to 0.976. To evaluate the generalization capability of the model, external validation was performed using experimental data of high-strength concrete reported in reliable studies. The results of this validation indicated a good agreement between the predicted values and the experimental data, confirming the accuracy and reliability of the proposed model .Finally, the results demonstrate the ability of the proposed model to improve the accuracy of concrete compressive strength prediction and enhance the generalization capability of machine learning models in concrete engineering problems.