During the 1970s, Brazil experienced most of its population living in cities for the first time. Historically, urbanization has been intensely related to land use and cover changes, amplifying climatic and ecological stress in recently established metropolises. This study aims to analyze, through classified Remote Sensing images and the Cellular Automata and Artificial Neural Networks (CA-ANN) machine learning model, land use and land cover trends in regions that have experienced accentuated demographic growth in the decades 2000 and 2010. The methodology consists of: i) identifying areas with a high density of buildings, ii) defining the variables that drive land use change, and iii) proposing a methodology for predicting changes in the urban area. The results indicate that the urban class prediction presented high precision (≥ 0.74) and recall (≥ 0.86) indices. Forest class also presented a high precision score (≥0.72), showing an elevated prediction hit rate. Furthermore, the proposed methodology improved the results obtained in previous works for the same cities, presenting higher Kappa values in all cases.