Coke quality has a critical role in the steelmaking industry. The aim of this study is to examine the complex relationships between various conventional coal analyses using coke making index “free swelling index (FSI)”. Random forest (RF) associated with variable importance measurements (VIMs), which is a new powerful statistical data mining approach, is utilized in this study to analyze a high-dimensional database (3961 samples) to rank variables, and to develop an accurate FSI predictive model based on the most important variables. VIMs was performed on various types of analyses which indicated that volatile matter, carbon, moisture (coal rank parameters) and organic sulfur are the most effective coal properties for the prediction of FSI. These variables have been used as an input set of RF model for the FSI modeling and prediction. Results of FSI model indicated that RF can provide a satisfactory prediction of FSI with the correlation of determination R2 = 0.96 and mean square error of 0.16 from laboratory FSIs (which is smaller than the interval unit of FSI; 0.5). Based on this result, RF can be used to rank and select effective variables by evaluating nonlinear relationships among parameters. Moreover, it can be further employed as a non-parametric reliable predictive method for modeling, controlling, and optimizing complex variables; which to our knowledge has never been utilized in the fuel and energy sectors.