Link Prediction (LP) in hypergraphs is a critical challenge in graph science, facilitating the identification of latent or emerging connections in complex systems. Unlike traditional graphs, hypergraphs inherently represent higher-order interactions, making LP more expressive but also computationally challenging. This problem has broad applications, including biological graphs (e.g., protein-protein interactions), social graphs (e.g., group collaborations), recommendation systems (e.g., multi-user preferences), and citation graphs (e.g., research co-authorships). Although hypergraph based LP methods extend beyond traditional pairwise graph based approaches by incorporating multi-node dependencies, they often fail to distinguish between densely connected intra-community structures and weak inter-community links, leading to an increase in false positives and reduced predictive accuracy. To mitigate this challenge, we introduce CCLPH (Community-Centric Link Prediction in Hypergraphs), a community driven LP framework that improves predictive accuracy by leveraging hypercommunity structures. Unlike traditional LP models that apply predictions globally, our approach prioritizes intra-community links, where connections are more likely to emerge, while filtering out low confidence inter-community predictions. By incorporating local similarity metrics and global ranking mechanisms, CCLPH effectively balances precision and recall, enhancing overall predictive performance. Our framework is evaluated using AUPR, AUROC, and F1-score, demonstrating its superior performance across various hypergraph datasets. Experimental results confirm that CCLPH consistently outperforms existing LP techniques by accurately modeling hypercommunity structures, resulting in more reliable and precise predictions. These findings underscore the significance of community aware LP in hypergraphs, establishing CCLPH as a robust and scalable framework for advancing link prediction in complex hypergraphs.