The grinding and classification processes are systematic engineering that must comprehensively consider the influence of several factors to ensure good grinding fineness. Based on the machine learning method, this study analyzed the full process parameters (i.e., ball mill power, fresh ore feed rate, hydrocyclone feed pump power, hydrocyclone pressure, mill feed water flow rate, dilution water flow rate, and sump level) for industrial grinding circuit. The collected real data (42,101 records) were employed to train and test the extreme gradient-boosting (XGBoost) regression model. The XGBoost model’s prediction ability and accuracy were evaluated and analyzed. The validated model was employed to evaluate the relative importance and influence mechanisms of process parameters. It was found that hydrocyclone feed pump power, dilution water flow rate, hydrocyclone pressure, and mill feed water flow rate significantly affected the grinding fineness, which were consistent with the actual operation of grinding circuit.