The rapid evolution of 5G networks mandates efficient strategies for Base Station (BS) deployment to ensure optimal Throughput as Quality of Service (QoS) parameter and coverage for diverse User equipment (UEs). This work evaluates three different Base Station (BS) placement strategies – Gridline, Random and optimized placement using K means clustering over a 1km area hosting 5000 UEs, simulation considers slice specific bandwidth allocation, mobility dynamics and coverage constraints for diverse service requirements. Findings highlights the inadequacy of traditional deployment strategies addressing the unique demands of 5G, Contrary to the traditional BS deployment strategies – Gridline and Random, the proposed K means clustering outperforms both achieving 20% average Throughput. It efficiently supports slice specific requirements, such as bandwidth, wide – area slices and 15% bandwidth, 5%-latency slices, catering to diverse use cases for applications like IoT, video streaming and Augmented Reality. By considering K means clustering, the approach offers a wide, adaptive framework for BS deployment in a dynamic environment, directing towards enhanced performance in the next generation networks, also it bridges the gap between theoretical advancements and practical 5G network challenges, setting the stage for smarter and more efficient wireless communication systems in the future.