The importance of spatial accuracy of land use/cover change maps necessitates the use of high performance models. To reach this goal, calibrating machine learning (ML) approaches to model land use/cover conversions have received increasing interest among the scholars. This originates from the strength of these techniques as they powerfully account for the complex relationships underlying urban dynamics. Compared to other ML techniques, random forest has rarely been used for modeling urban growth. This paper, drawing on information from the multi-temporal Landsat satellite images of 1985, 2000 and 2015, calibrates a random forest regression (RFR) model to quantify the variable importance and simulation of urban change spatial patterns. The results and performance of RFR model were evaluated using two complementary tools, relative operating characteristics (ROC) and total operating characteristics (TOC), by overlaying the map of observed change and the modeled suitability map for land use change (error map). The suitability map produced by RFR model showed 82.48% area under curve for the ROC model which indicates a very good performance and highlights its appropriateness for simulating urban growth.