The membrane water content of the proton exchange membrane fuel cell (PEMFC) is the most important feature required for water management of the PEMFC system. Any improper management of water in the fuel cell may lead to system faults. Among various faults, flooding and drying faults are the most frequent in the PEMFC systems. This paper presents a new dynamic semi-empirical model which requires only the load current and temperature of the PEMFC system as the input while providing output voltage and membrane water content as its major outputs. Unlike other PEMFC systems, the proposed dynamic model calculates the internal partial pressure of oxygen and hydrogen rather than using special internal sensors. Moreover, the membrane water content and internal resistances of PEMFC are modelled by incorporating the load current condition and temperature of the PEMFC system. The model parameters have been extracted by using a quantum lightening search algorithm as an optimization technique, and the performance is validated with experimental data obtained from the NEXA 1.2 kW PEMFC system. To further demonstrate the capability of the model in fault detection, the variation in membrane water content has been studied via the simulation. The proposed model could be efficiently used in prognostic and diagnosis systems of PEMFC fault.