The number of variables involved in the formation of Ohmic contacts to SiC is large, and their relationships to the final contact resistance are often unclear. As such, trial-and-error methods are typically employed to develop or improve SiC contacts. In pursuit of a better alternative, we developed and tested several regression models to predict the specific contact resistance of Ni, Ti, and Al based contacts on both n- and p-type SiC. Literature data was used to train linear regression, Gaussian process regression, and neural network (NN) ensemble models; of these, the NN ensemble was the most effective at predicting contact resistances. We then applied the model to optimize the annealing schedule for Ni contacts to n-type 4H-SiC, and Ti/Al contacts to p-type 4H-SiC. Finally, we use the model to generate optimal simultaneous contact recipes.