As a core component of power electronic systems, insulated gate bipolar transistors （IGBT） are susceptible to performance degradation and failure due to electro-thermal stress during practical applications, making accurate remaining useful life prediction crucial. To address the insufficient prediction accuracy of single models in insulated gate bipolar transistors lifetime prediction, this paper investigates a multi-model fusion approach for remaining useful life prediction. The method first employs variational mode decomposition （VMD） to decompose the collector-emitter transient peak voltage, a key characteristic parameter for insulated gate bipolar transistors lifetime prediction, into multiple intrinsic mode components. The low-frequency trend component is predicted using a Gaussian process regression model, while the high-frequency fluctuation components are modeled using neural basis expansion analysis for time series (NBEATS) network. The final prediction is obtained by reconstructing and fusing the predictions of all components. Validation using NASA’s IGBT accelerated aging experimental data shows that the proposed fusion model achieves a 70% reduction in root mean square Error, a 23.2% decrease in mean absolute error, and an improvement in the coefficient of determination to above 0.97 compared to the best single VMD-NBEATS model. By varying the ratio between training and testing sets, the fusion model consistently demonstrates superior performance across different proportions, validating the stability and generalizability of the multi-scale decomposition and differentiated modeling approach. This work provides a novel solution for health monitoring and preventive maintenance of power electronic devices.