Handover (HO), as a key aspect of mobility management, plays an important role in improving network quality and mobility performance in mobile networks. Especially, in 5G networks, heterogeneous networks (HetNets) deployment of macro cells and small cells, and the deployment of ultra-dense networks (UDNs) make HO management become more challenging. Besides, the understanding of HO behavior in a cell is quite limited in existing studies, thus the forecasting HO for an individual cell is complicated, even impossible. This challenge led the authors to propose a practical process for managing and forecasting HO for a huge number of cells, based on machinelearning (ML) algorithms and big data. Moreover, based on HO forecasting, the authors also propose an approach to detect any abnormal HO in cells. The performance of the proposed approaches was evaluated by applying it to a real dataset that collected HO KPI of more than 6000 cells of a real network during the years, 2016 and 2017. The results show that the study was successful in identifying, separating HO behavior, forecasting the future number of HO attempts, and detecting abnormal HO behaviors of cells.