During the past decades, urban growth has been accelerating with the massive immigration of population to cities. Urban population in the world was estimated as 2.9 billion in 2000 and predicted to reach 5.0 billion in 2030. Rapid urbanization and population growth have been a common phenomenon, especially in the developing countries such as Iran. Rapid population growth, environmental changes and improper land use planning practices in the past decades have resulted in environmental deterioration, haphazard landscape development and stress on the ecosystem structure, housing shortages, insufficient infrastructure, and increasing urban climatological and ecological problems. In this study, urban sprawl assessment was implemented using Shannon entropy and then, Artificial Neural Network (ANN) has been adopted for modeling urban growth. Our case study is Tehran Metropolis, capital of Iran. Landsat imageries acquired in 1988, 1999 and 2010 are used. According to the results of sprawl assessment for this city, this city has experienced sprawl between 1988 to 2010. Dataset include distance to roads, distance to green spaces, distance to developed area, slope, number of urban cells in a 3 by 3 neighborhood, distance to fault and elevation. Relative operating characteristic (ROC) method have been used to evaluate the accuracy and performance of the model. The obtained ROC equal to 0.8366.