In this study, plasma nitriding was used to fabricate a hard protective layer on AISI P20 steel, at three process temperatures(450℃, 500℃, and 550℃) and over a range of time periods(2.5, 5, 7.5, and 10 h), and at a fixed gas N_2:H_2 ratio of 75vol%:25vol%. The morphology of samples was studied using optical microscopy and scanning electron microscopy, and the formed phase of each sample was determined by X-ray diffraction. The elemental depth profile was measured by energy dispersive X-ray spectroscopy, wavelength dispersive spectroscopy, and glow dispersive spectroscopy. The hardness profile of the samples was identified, and the microhardness profile from the surface to the sample center was recorded. The results show that ε-nitride is the dominant species after carrying out plasma nitriding in all strategies and that the plasma nitriding process improves the hardness up to more than three times. It is found that as the time and temperature of the process increase, the hardness and hardness depth of the diffusion zone considerably increase. Furthermore, artificial neural networks were used to predict the effects of operational parameters on the mechanical properties of plastic mold steel. The plasma temperature, running time of imposition, and target distance to the sample surface were all used as network inputs; Vickers hardness measurements were given as the output of the model. The model accurately reproduced the experimental outcomes under different operational conditions; therefore, it can be used in the effective simulation of the plasma nitriding process in AISI P20 steel. In this study, plasma nitriding was used to fabricate a hard protective layer on AISI P20 steel, at three process temperatures (450 ° C, 500 ° C, and 550 ° C) and over a range of time periods (2.5, 5, 7.5, and 10 h and at a fixed gas N_2: H_2 ratio of 75 vol%: 25 vol%. The morphology of samples was studied using optical microscopy and scanning electron microscopy, and the formed phase of each sample was determined by X-ray diffraction. The elemental The depth profile was measured by energy dispersive X-ray spectroscopy, wavelength dispersive spectroscopy, and glow dispersive spectroscopy. The results show that ε- nitride is the dominant species after carrying out plasma nitriding in all strategies and that the plasma nitriding process improves the hardness up to more than three times. It is found that as the time and temperature of the process increase, the hardnes s, hardness and depth of the diffusion zone considerably increase. Furthermore, artificial neural networks were used to predict the effects of operational parameters on the mechanical properties of plastic mold steel. The plasma temperature, running time of imposition, and target distance to the sample surface The model accurately reproduced the experimental outcomes under different operational conditions; therefore, it can be used in the effective simulation of the plasma nitriding process in AISI P20 steel .