Fuel cells transform the chemical energy of hydrogen directly into electrical energy without ignition or thermal processes. Their behavior is defined based on electrochemistry and thermodynamics that involves complex computations in their mathematical model. This problem of modeling can be resolved by using soft computing techniques. Fuel cells are effective, versatile and silent devices that can provide power to many applications¸ from portable electronic devices to automobiles, to electrical grids across the nation. Due to the nonlinear process of a fuel cell, fuzzy logic, neural network, and Neurofuzzy controllers are suitable for regulating input gasses flow rate to get appropriate electrical power according to load demand. This paper describes aMATLAB / Simulink model of 1KW, 28.8V DC power PEM fuel cell for controlling hydrogen flow rate to the fuel cell stack using fuzzy logic, neural network, and Neuro-fuzzy controllers. The output performance of controllers is compared based on their efficiency and utilization. Simulation results showed that the Neuro-Fuzzy controller provides good performance for the purging process of hydrogen