The purpose of this work is to determine the optimal grinding parameters for processing the end faces of silicon nitride ceramic bearing rings and to construct a predictive model of surface roughness. First, a double-face grinder is used to carry out multiple sets of single factor experiments on silicon nitride ceramic bearing rings. The two variables set in the experiment are the grinding wheel speed and the grinding wheel feed speed, and four processing parameter levels are set for the two variables respectively. Analyze the influence of the grinding wheel feed speed and the grinding wheel speed on the surface quality after processing, and then use the toolbox in MATLAB to construct the surface roughness prediction model. The optimal processing parameters obtained through experiments are:the grinding wheel speed is 1400 r/min, the grinding wheel feed speed is 200 μm/min, and the optimal surface roughness obtained is 0.0827 μm, which is in line with the quality of high-precision all-ceramic bearing end faces in engineering Claim. This paper established a surface roughness prediction model and optimized the prediction model. Compared with the actual measured surface roughness value, the absolute value of the optimized prediction model has the smallest relative error of –0.56%, and the maximum error of Ra is 0.0113 μm. According to the experimental results, the surface roughness is negatively correlated with the speed of the grinding wheel and the feed speed of the grinding wheel. From the experimental results and the prediction model, it can be seen that as the speed of the grinding wheel and the feed speed of the grinding wheel increase, the surface roughness value Ra shows a downward trend. It is concluded that when grinding the end face of the silicon nitride ceramic bearing ring, appropriately increasing the speed of the grinding wheel and the feeding speed of the grinding wheel can help reduce the surface roughness and improve the surface quality.