Missing substructure information has impeded the safety assessment of bridges with unknown foundations, especially for scour-prone bridges. An approach based on artificial neural networks (ANNs) was developed to identify the inherent patterns in the substructure design of bridges with commonly available evidence (e.g., geometric characteristics of superstructures, loading conditions, soil properties, year built, and location) and then to generalize them further to bridges with unknown foundations. The proposed ANN models were trained with information collected for an inventory of bridges with available foundation records located in the Bryan District of the Texas Department of Transportation. Results showed that the proposed ANN models were able to make successful predictions about the foundation type and the embedment depth for deep foundations. In addition, the degree of uncertainty in the models predictions was evaluated by performing the random subsampling method. Graphs of the probability of exceedance were generated that allowed for factoring the predicted pile depth on the basis of a reasonable probability of failure caused by scour. As a consequence, departments of transportation can adopt a similar methodology to reclassify the bridges with unknown foundations included in the National Bridge Inventory.