This paper proposes an AI solution to optimize the site selection process for 5G Radio Network(RAN) planning. We study various analytic and machine learning techniques to accurately identify the demand for 5G, and use that to plan for optimum 5G site selection, with an aim to have a highest possible return on investment (ROI). The proposed approach first detects the relationship between various network attributes, such as cell performance counters, customer behaviour, handsets’ penetration, and their effect on the expected 5G network load. Then it clusters the cells according to their priorities and required quality of services. It then uses the supervised model to predict and simulate the expected movement of the user to the5G layer, and at the same time, predict the expected change in 4G network performance. Finally, it incorporates the result into a ranking metric with a scoring schema, and provides a list of 5G cell candidates for upgrade considerations. Experimental results are presented to show the validity of the approach.