In this study, we introduce a novel approach to influence optimization in social networks by leveraging the mathematical framework of hypergraphs. Traditional centrality measures often fall short in capturing the multi-dimensional nature of influence. To address this gap, we propose the Spreading Influence (SI) model, a sophisticated tool designed to quantify the propagation potential of nodes more accurately within hypergraphs. Our research embarked on a comparative analysis using the Susceptible-Infected-Recovered (SIR) model across four distinct scenarios-where the top 5, 10, 15, and 20 nodes were initially infected-in four diverse datasets: Amazon, DBLP, Email-Enron, and Cora. The SI model’s performance was benchmarked against established centrality measures: Hyperdegree Centrality (HDC), Closeness Centrality (CC), Betweenness Centrality (BC), and Hyperedge Degree Centrality (HEDC). The findings underscored the SI model’s consistently superior performance in predicting influence spread. In scenarios involving the top 10 nodes, the model exhibited up to 3.18% increased influence spread over HDC, 2.14% over CC, 1.04% over BC, and 1.69% over HEDC. This indicates a substantial improvement in identifying key influencers within networks.