This article provides an in-depth analysis of the fatigue characteristics of asphalt mixtures and points out that fatigue failure is one of the common main problems in asphalt pavements. Due to significant differences between the environmental conditions of indoor experiments and actual road conditions, the experimental results cannot accurately predict the actual fatigue performance of asphalt pavement. Therefore, it is necessary to make more accurate fatigue performance predictions under multiple factor conditions. Based on a thorough study of existing literature, this article proposes an optimized prediction model by combining neural networks with empire competition algorithms. Four neural network models, BP, RBF, ICA-BP, and ICA-RBF, were constructed using MATLAB software to simulate the fatigue characteristics of asphalt mixtures and adjust the model parameters to improve prediction accuracy. Finally, the ICA-BP model was found to have the best predictive performance through error analysis. This article also derived an estimation formula for fatigue characteristics through the model and analyzed the influence of sample size on prediction error. This study not only provides new ideas for predicting the fatigue performance of asphalt mixtures, but also lays the foundation for related engineering applications and future research.