This paper introduces TLPSC, a novel clustering algorithm that combines Particle Swarm Clustering (PSC) with Prototype-Based Transfer Learning (PBTL). By leveraging knowledge transfer from a source domain, TLPSC enhances cluster quality, especially in scenarios with sparse or noisy data. Experimental results show TLPSC outperforms traditional clustering algorithms, such as K-Means, Gaussian Mixture Models (GMM), and the standard PSC, across some evaluation metrics, including NMI, ARI, Silhouette, and Davies-Bouldin. TLPSC shows its ability to maintain the data structure and generate cohesive clusters, proving to be a robust and efficient solution for clustering tasks.