The increasing global energy demand and the growing environmental problems have intensified the pursuit of clean and sustainable energy solutions. Hydrogen, with its high energy density and clean by-products, is a promising candidate as an energy source. Fuel cells play a key role in harnessing hydrogen energy, but this technology faces challenges such as the trade-off between material stability and ion conductivity, which limits its widespread application. To address these challenges, designing material properties and adjusting system parameters are highly desirable. However, the traditional trial-and-error approach is no longer feasible when dealing with the vast array of possibilities. Fortunately, the advancement of artificial intelligence (AI) offers a new approach which can dramatically speed up the material design and parameter control. This article reviews the application of AI in fuel cells, especially its ability to accelerate material development. The review begins by outlining the mechanisms and classifications of fuel cells, as well as the property requirements for each part of the fuel cells. Subsequently, the article introduces the basic concepts of AI and its application in materials science, including the workflows of data aggregation, feature construction, model training, and experimental validation. Importantly, the applications of AI in predicting fuel cell material performance are highly emphasized and discussed. In addition, the challenges encountered in AI applications are introduced, including sparse datasets, complex feature engineering, the limitations of general models, and the weak interpretability of AI models, along with their respective development blueprints.