The long-term performance of three different automotive surface coatings (physical barrier, sacrificial, and hybrid) was predicted using electrochemical impedance spectroscopy (EIS). Corrosive conditions faced by vehicles in the field, such as deicing, can be simulated using accelerated methods. The coating/metallic substrate interface experiences various degradation mechanisms during exposure to harsh conditions. In this work, real-time measurements were performed via EIS testing to characterize the degradation and corrosion mechanism of coating and substrate. After the real-time measurements, a mathematical framework based on mechanistic and machine-learning concepts was developed. Phase angle plots from EIS were utilized to monitor the state of the coating during steady-state conditions and train the Artificial Neural Network (ANN) as an arrangement of Time Series Prediction (TSP). The transport processes, activation, and interface interaction with the corrosive environments were analyzed as a corrosion mechanism and were predicted via the ANN model. The ANN has predicted the coating performance for several years, and the experimental results have been validated by employing scanning electron microscopy (SEM) imaging. Each coating condition has been validated via SEM imaging at the initial state and when the coating protection is activated.