Estimation of mill power-draw can play a critical role in economics, operation and control standpoints of the entire mineral processing plants since the cost of milling is the single biggest expense within the process. Thus, several empirical power-draw prediction models have been generated based on a combination of laboratory, pilot and full-scale measurements of different milling conditions. However, they cannot be used in industrial plants, where in full-scale operations, only not few numbers of input parameters used in those models are measured. Moreover, empirical models do not assess the relationship between input features. This investigation is going to introduce random forest (RF), as a predictive model, beside of its associated variable importance measures system, as a sensible means for variable selection, to overcome drawbacks of empirical models. Although RF as a powerful modeling tool has been used in several problem solving systems, it has not comprehensively considered in the powder technology areas. In this investigation, an industrial ball mill database from Chadormalu iron ore processing plant were used to develop a RF model and explore relationships between power-draw and other monitored operating parameters. Modeling results indicated that RF can highly improve the prediction accuracy of power-draw as compared to the regression as a typical method (R2: 0.98 vs. 0.60, respectively) and rank operational milling parameters based on their importance.