In this paper, a Multi-Reservoir Echo State Network (MR-ESN) is used to estimate the Fuel Cell FC degradation, and its remaining useful lifespan, RUL. It proposes a methodology for predicting the fuel cell output voltage evolution with time. Echo State Network, ESN, is a powerful Artificial Intelligence, AI, tool for time series predicting which main characteristics is the use of a reservoir of neurons, randomly created, instead of hidden layers such as for Artificial Neural Networks, ANN. Only the output layer is optimized by a multi-linear regression, resulting in a time reduced training phase. This leads to a possible increase of the reservoir size to preserve, even improve, the accuracy. However, the bottleneck linked to the use of this tool lies in its architecture optimization. This paper proposes a way to overcome the ESN parameters optimization process by using a MR-ESN. Then a comparison between an ESN optimized algorithm and a MR-ESN for fuel cell RUL prediction is proposed. In order to have a good prediction of the FC lifetime, an innovative approach based on the MR-ESN is developed and validated using experimental data.