This study employs artificial neural networks (ANNs) to predict the fatigue life of asphalt concrete (AC), crucial for road maintenance and longevity. Leveraging a dataset from extensive laboratory tests, we optimized ANN models to address the variability in AC fatigue data. Our approach involved fine-tuning hyperparameters and adapting network architectures to best utilize a dataset of 152 samples. The models were trained with both linear and logarithmic loss functions. Results showed that modified bituminous binders significantly improve fatigue life predictions, with comprehensive input parameters being vital for accurate modeling. Although models achieved moderate overall \(R^2\) scores of about 0.4, they highlighted the significant impact of binder type and content on prediction outcomes. The research demonstrates the potential of machine learning to enhance pavement engineering by providing deeper insights into AC fatigue life. Optimized models and codes are available in an open repository, encouraging further exploration and application.