Spheroidizing annealing is a well-known heating method used for improving the ductility of steel so that the steel can be easily machined or deformed. In the annealing process, to avoid the high-temperature oxidation of steel, hydrogen and nitrogen are often used as protective reducing gases during annealing. A certain amount of hydrogen is allowed to flow continuously into the furnace. However, owing to the high cost of hydrogen and production safety, the flow and amount of hydrogen used in the annealing process should be effectively controlled. In this work, we present a study of hydrogen concentration prediction using neural networks (NNs). In the spheroidizing annealing process, three parameters, namely, base motor power, base motor speed, and inner temperature, are used for the immediate prediction of hydrogen concentration in the furnace. Once the hydrogen in the furnace reaches the specified concentration, the flow rate of hydrogen should be reduced. In this study, the data of hydrogen concentration was collected using an XMTC sensor, a thermal conductivity transmitter that can measure the concentration of binary gas mixtures containing hydrogen, carbon dioxide, methane or helium. From simulation results, it was found that the NN model can indeed provide a fairly accurate prediction of the hydrogen concentration on the basis of the physical characteristics of the motor. The results of this study showed that the application of artificial intelligence in predicting the hydrogen concentration in the annealing process is very promising and feasible.