This study focuses on the aging evaluation of Insulated gate bipolar transistor (IGBT) modules to ensure their stability during operation. An aging degree evaluation model is proposed based on whale optimization algorithm optimized extreme learning machine (WOA-ELM) algorithm. This study is mainly concentrated on two aspects. One is to use WOA to optimize the input weights and hidden layer biases of ELM to improve its prediction performance. This study tested the performance of WOA-ELM on several benchmark datasets. The results show that the prediction performance of WOA-ELM is better than ELM, genetic algorithm optimized ELM, cuckoo search optimized ELM, and dandelion algorithm optimized ELM. The other is to measure the electrical and thermal characteristic data of IGBT module under different aging conditions by accelerated aging test. Based on the analysis of the experimental data under different aging degrees, a method for evaluating the aging degree of IGBT modules based on WOA-ELM is proposed. Simulation results based on experimental data show that WOA-ELM still has better accuracy and generalization performance than others. In summary, the WOA-ELM algorithm is applicable to the aging evaluation method of IGBT modules proposed in this study which has good practical value.