Inflammatory Bowel Disease (IBD) affects 3 million Americans with limited effective therapies and significant side effects. Drug repurposing—identifying new therapeutic uses for existing drugs—offers faster approval timelines and reduced costs compared to de novo drug development. We present a network pharmacology approach combining protein-protein interaction (PPI) data, drug-target information, and disease-gene networks to systematically identify existing drugs for IBD. Our method calculates network proximity scores (Guney et al. 2016) based on the shortest paths between drug targets and disease genes within the STRING PPI database. We evaluate 7 clinically-relevant drugs including approved therapeutics (infliximab, vedolizumab), experimental agents (thalidomide, hydroxychloroquine), and repurposing candidates (metformin, aspirin). Results identify infliximab and metformin as top candidates with highest network proximity to IBD disease genes (NOD2, ATG16L1, IL23R). We construct drug-target-disease networks revealing direct interactions between drug targets and inflammatory mediators (TNF, IL-6, NF-κB). This work demonstrates that computational network analysis can prioritize drug candidates for experimental validation, offering a rapid, cost-effective approach to identify existing therapeutics for IBD.