The fifth-generation (5G) of networks is being deployed to provide a wide range of new services and to manage the accelerated traffic load of the existing networks. In the present-day networks, data has become more noteworthy than ever to infer about the traffic load and existing network infrastructure to minimize the cost of new 5G deployments. Identifying the region of highest traffic density in megabyte (MB) per <inline-formula> <tex-math notation="LaTeX">\text {km}^{2} </tex-math></inline-formula> has an important implication in minimizing the cost per bit for the mobile network operators (MNOs). In this study, we propose a base station (BS) clustering framework based on unsupervised learning to identify the target area known as the highest traffic cluster (HTC) for 5G deployments. We propose a novel approach assisted by real data to determine the appropriate number of clusters <inline-formula> <tex-math notation="LaTeX">k </tex-math></inline-formula> and to identify the HTC. The algorithm, named as NetClustering , determines the HTC and appropriate value of <inline-formula> <tex-math notation="LaTeX">k </tex-math></inline-formula> by fulfilling MNO's requirements on the highest traffic density <inline-formula> <tex-math notation="LaTeX">\text {MB/km}^{2} </tex-math></inline-formula> and the target deployment area in <inline-formula> <tex-math notation="LaTeX">\text {km}^{2} </tex-math></inline-formula>. To compare the appropriate value of <inline-formula> <tex-math notation="LaTeX">k </tex-math></inline-formula> and other performance parameters, we use the Elbow heuristic as a benchmark. The simulation results show that the proposed algorithm fulfills the MNO's requirements on the target deployment area in <inline-formula> <tex-math notation="LaTeX">\text {km}^{2} </tex-math></inline-formula> and highest traffic density <inline-formula> <tex-math notation="LaTeX">\text {MB/km}^{2} </tex-math></inline-formula> with significant cost savings and achieves higher network utilization compared to the Elbow heuristic. In brief, the proposed algorithm provides a more meaningful interpretation of the underlying data in the context of clustering performed for network planning.