Polycystic Ovary Syndrome (PCOS) is a complex endocrine disorder affecting up to 10% of women of reproductive age, with early detection remaining challenging due to its heterogeneous presentation. Traditional diagnostic approaches relying solely on clinical markers often miss subtle molecular indicators, while genomic analyses alone fail to incorporate crucial clinical context, resulting in delayed diagnosis and suboptimal treatment outcomes. This project addresses these limitations through a novel multi-modal approach integrating omics data (from GEO datasets including GSE5090, GSE54248, GSE54250) with clinical biomarkers using a sophisticated architecture combining Graph Neural Networks (GNNs) and feed-forward networks. The implementation features separate processing pathways: omics data flows through GNN layers (GATConv/GCNConv) modeling gene-gene interactions, while clinical markers pass through conventional neural networks. These pathways converge in a fusion mechanism with adaptive weighting based on clinical indicators like AMH levels. Domain adaptation techniques mitigate batch effects across datasets, while transfer learning leverages pre-trained encoders to improve generalizability. Comprehensive evaluation demonstrates the system's effectiveness with a cross-validated mean AUC of 0.653 (±0.116). Refined threshold tuning yields a precision of 0.975, recall of 0.870, and classification accuracy up to 95%. The integration of explainable AI tools provides transparent visualization of feature importance, showing that AMH levels, β-HCG measurements, and their derived ratios consistently emerge as critical predictors. This approach not only enhances diagnostic accuracy but also provides actionable insights into the molecular mechanisms underlying PCOS.