The paper describes the use of artificial neural networks (ANNs) as pavement structural analysis tools for the rapid and accurate prediction of critical responses and deflection profiles of flexible pavements subjected to typical highway loadings. The ILLI-PAVE finite element program, extensively tested and validated for over three decades, was used as an advanced structural model for solving critical responses of flexible pavement responses with unbound aggregate layers. Unlike the linear elastic layered theory commonly used in pavement layer back calculation, nonlinear unbound aggregate base (UAB) and subgrade soil response models were used in the ILLI-PAVE program to account for the typical stiffening behavior of UABs and the fine-grained subgrade soil moduli decreasing with increasing stress states. ANN models then trained with the results from the ILLI-Pave solutions have been found to be viable alternatives. The pavement deflection profiles could be only predicted with the proper characterization of nonlinear stress-dependent UABs and subgrade soils in the trained ANN models. The trained ANN models were also capable of rapidly predicting critical pavement responses with low average errors of those obtained directly from the ILLI-Pave analysis