Health monitoring has gained a massive interest in power systems engineering, as it has the advantage to reduce operating costs, improve reliability of power supply and provide a better service to customers. This paper presents surrogate methods to predict the electrical insulation lifetime using the neural network approach and three curve fitting models. These can be used for the health monitoring of insulating systems in electrical equipment, such as motors, generators, and transformers. The curve fit models and the supervised backpropagation neural network are employed to predict the insulation resistance trend of enameled copper wires, when stressed with a temperature of 290 °C. After selecting a suitable end of life criterion, the specimens' mean time-to-failure is estimated, and the performance of each of the analyzed models is apprised through a comparison with the standard method for thermal life evaluation of enameled wires. Amongst all, the best prediction accuracy is achieved by a Backpropagation neural network approach, which gives an error of just 3.29% when compared with the conventional life evaluation method, whereas, the error is above 10% for all the three investigated curve fit models.