In this work, optimal modeling of deep groove ball bearings is examined for application in multibody dynamics simulations. First, the equations for the normal contact force between the rolling elements and the two bearing races are established, including the effects of raceway surface roughness. Various contact force models with different hysteresis damping formulations are examined in order to select the best suited for the application. The bearing contact force model is then used in a multibody dynamics simulation of a bearing test-rig, aiming to estimate the model’s optimal parameters resulting in a good approximation of the system’s behavior and, finally, to a well-calibrated ready-to-use bearing model. The optimal model is then used to examine the system’s behavior in the presence of defects in the bearings, validating the robustness and performance of the optimized deep groove ball bearing model. The system’s response is examined by means of signal analysis as well as by using deep learning methods in order to characterize the health state of the system, thus proving the applicability of the present bearing modeling method for condition monitoring applications.