The proton exchange membrane fuel cell (PEMFC) is a critical and essential component of a zeroemission electro-hydrogen generator. The accurate prediction of its performance is important for optimal operation management and preventive maintenance of the system. However, the prediction is not easy because the PEMFCs have complex electrochemical reactions with multiple nonlinear relations between operating variables as inputs and voltage as output. In this paper, we propose an efficient prediction approach based on XGBRegressor and Tree-structured Parzen Estimator. In addition, to better select relevant features, Kernel Principal Component Analysis and Mutual Information are jointly used. The proposed approach allows considering the dynamic operating conditions of the fuel cells. To test and validate the robustness of the proposed approach, a data-set of ten PEMFCs was used. Furthermore, a comparison study with traditional machine learning models, such as Artificial Neural Networks and Support Vector Machine Regressors is investigated. It was shown that the proposed approach provides better results.