The potential of Markov chain and cellular automata model with help of agents that play a vital role in a cities urbanisation through fuzziness in the data and hierarchal weights (for principal agents) have been used to understand and predict the urban growth for the Pune city, India. The model utilizes temporal land use changes with probable growth agents such as roads drainage networks, railway connectivity, slope, bus network, industrial establishments, educational network etc., to simulate the growth of Pune for 2025 using two scenarios of development – implementation of City Development Plan (CDP) and without CDP. In the study, multi temporal land use datasets, derived from remotely-sensed images of 1992, 2000, 2010 and 2013, were used for simulation and validation. Prediction reveals that future urban expansion would be in northwest and southeast regions with intensification near the central business district. This approach provides insights to urban growth dynamics required for city planning and management.