Faced with the climate emergency and the energy transition, proton exchange membrane fuel cells (PEMFC) represent a key solution for clean electricity production from hydrogen. However, their mass deployment is hampered by technical challenges, notably membrane moisture management and early detection of failures (flooding, dehydration). This paper explores the use of artificial intelligence (AI) to optimize the identification of critical electrochemical parameters (ohmic resistance, double-layer capacitance) via impedance spectroscopy. Traditional methods, while accurate (squared error ∼10-⁸), suffer from prohibitive computation times (2–3 h) and an inability to predict failures in real time. To remedy this, hybrid approaches combining genetic algorithms (GA) and neural networks (LSTM, CNN) reduce analysis time to less than 10 min, with an accuracy adapted to industrial needs (∼10-⁶). A case study demonstrates that a CNN model achieves 98% accuracy in flooding detection, thanks to automated Nyquist diagram analysis. In addition, the integration of digital twins enables dynamic simulation of PEMFCs, optimizing their performance and anticipating aging. Future prospects include Edge AI for embedded monitoring, data federation for generalizable models, and adaptive twins coupled with recurrent networks. These advances position AI as a key pillar for accelerating the adoption of PEMFCs in electric vehicles and stationary storage systems, supporting Europe's transition to hydrogen, whose automotive market is expected to grow by 14.1% by 2032.