The resilient modulus (MR) is a fundamental material property that has a direct effect on the design and analysis of pavement structures. Many regression models have been developed previously to predict the coefficients of the MR model from physical properties of base materials. However, the predicted model coefficients are confined to either a limited number of base materials or result in poor accuracy. To overcome this issue, a moisture- and stress-dependent model is adopted in this study to precisely estimate MR of unbound base materials in unsaturated conditions, and a set of artificial neural network (ANN) models is developed to predict the coefficients of this model from base physical properties. The developed ANN models consist of seven input variables, ten hidden neurons, and one output variable. A large unbound base dataset was collected from the Long Term Pavement Performance (LTPP) database and used to train and generalize the network. Soil physical properties such as gradation (percent passing No. 3/8 sieve, percent passing No. 200 sieve), gradation shape parameter and scale parameter, index properties (i.e., plastic limit and plasticity index), maximum dry density, optimum moisture content, and test moisture content were selected as inputs for the ANN model. The MR values estimated using the predicted coefficients were compared with the experimental data collected from LTPP and showed an R2 value above 0.9, which is much higher than the MR values computed using regression models. Finally, the MR test results from different sources were used to validate the developed ANN models.