Understanding stress-strain behavior of asphalt pavement under repetitive traffic loading is of critical importance to predict pavement performance and service life. For viscoelastic materials, the stress-strain relationship can be represented by the dynamic modulus. The dynamic modulus test in indirect tension mode can be used to measure the modulus of each specific layer of asphalt pavements using representative samples. Dynamic modulus is a function of material properties, loading, and environmental conditions. Developing predictive models for dynamic modulus is efficient and cost effective. This article focuses on developing an accurate Finite Element (FE) model using mixture elastic modulus and asphalt binder properties to predict dynamic modulus of asphalt mix in indirect tension mode. An Artificial Neural Network (ANN) is used to back-calculate the elastic modulus of asphalt mixtures. The developed FE model was verified against experimental results of field cores from nine different pavement sections from five districts in the State of Minnesota. It is demonstrated that the ANN modeling is a powerful tool to back-calculate the elastic modulus and FE model is capable of accurately predicting dynamic modulus.