Asphalt pavement is a complex engineering system which deteriorates due to several mechanical and environmental stressors (e.g. moisture damage, freeze-thaw cycles and traffic load). To predict the time-dependent performance of asphalt pavement, it is necessary to develop a deterioration model incorporating the associated variables under uncertainty. Artificial Neural Networks (ANNs) are effective intelligence technologies to develop an accurate prediction model with a large amount of data. In this paper, a time-dependent reliability assessment method based on the ANNs model is presented. ANNs are used to develop the performance prediction model of asphalt pavement according to the training data selected from the Long-term Pavement Performance database. The life-cycle reliability of asphalt pavement is calculated using the ANNs model based on Monte Carlo simulation with Importance Sampling. Two case studies are presented to investigate the effects of sublayers thickness and traffic levels on the life-cycle reliability.