To further improve the prediction accuracy of stack degradation and remaining useful life of proton exchange membrane fuel cell (PEMFC) for vehicles, a novel vehicle PEMFC degradation prediction method is proposed based on convolutional neural network-long short-term memory (CNN-LSTM) optimized by improved grey wolf algorithm (IGWO). First, the data set is denoised and reconstructed using stationary wavelet transform, and IGWO is then used to analyze the measured PEMFC stack degradation data to obtain the optimal hyperparameters for the CNN-LSTM model. Next, the CNN-LSTM network model is trained with the optimal hyperparameters to predict PEMFC performance degradation and calculate the remaining useful life of the PEMFC stack. Finally, under both static and dynamic operating conditions, the proposed method is compared with traditional LSTM, gated recurrent unit network, and unoptimized CNN-LSTM models. Results show that under static conditions with a 60 % training set ratio, the proposed method reduces the root mean square error by 59.02 % compared with the traditional CNN-LSTM. With a 70% training set ratio, the predicted remaining service life differs from the actual value by only 1.16 hours. Under dynamic conditions with a 40% training set ratio, the average absolute error is reduced by 18.78 %.