In the realm of machine learning (ML), clustering is a unsupervised ML technique which involves grouping of related objects with certain similar features. Since clustering is a recursive process, it is a time consuming task especially for large datasets because traditional approaches work sequentially, so clustering tasks give out with a lot of delay and therefore addressing these challenges involve a lot of domain exploration. Optimization in ML is a paramount task where it specifically focuses on efficiency of the model involving adjustments of hyperparameters. Cluster optimization plays a pivotal role in cluster formation affecting the quality and number of clusters for diverse data. Considering the power and performing efficiency of quantum computing that leveraging quantum mechanical principles, workings parallel and performs tasks simultaneously. The key components include qubits, superposition, entanglement, quantum speedup and so on. This research paper presents quantum-inspired clustering techniques using traditional K-means technique and quantum K-means technique. Moreover, it includes summary of each technique including the working flow, pros, drawbacks also demonstrating why quantum K-means performs more efficiently than K-means technique, potentially performed with quantum principles. This research also examines that various settings impact the workflow of the algorithms. This finding indicates that these factors significantly affect the cluster formations. Moreover, this research paper served as an insightful guidance for researchers in the future study in similar domains like social network analysis, genomics and bio-informatics, health care and medical research, image and video, Geographic Information System.