Unmanaged and uncontrolled urbanization has led to chaotic growth causing extensive fragmentation altering the local land use dynamics and the environment in and around many cities across the globe and has reached crucial thresholds. Quantify-ing these spatiotemporal urban growth patterns and visualisation is essential to understand the dynamics of urbanization and landscape dynamics. In this study, we use remotely sensed data to understand and visualise the urban growth patterns of Bangalore- Silicon hub of India. It has been stated that population growth and huge investments from the global markets are driving the change in land-use in Bangalore with the influx of population increased by 200% in the last decade. Considering this aspect in this study, Cellular Automata based model with the integration of socioeconomic factors was calibrated using historical urban growth extracted from classified data. This is used to forecast three scenarios of urban growth to 2020 with constraints as per the City Development Plan. Time series analysis of land use change exhibited an extensive outgrowth and urban sprawl in Bangalore: there has been leapfrog development in core regions of the city, whereas the buffer zones had ribbon development and cluster-based development. Urban sprawl was more along the major roads and places with better connectivity. Modelled land use results indicated an increase in the paved surface by 170% in the scenario, as usual, is con-sidered. Because of models highly explicit nature of prediction, it was able to capture both linear and non-linear behaviour and a phase transition that happens in the urban landscape.