Drug repurposing has historically been an economically infeasible process for identifying novel uses for abandoned drugs. Modern machine learning has enabled the identification of complex biochemical intricacies in candidate drugs; however, many studies rely on simplified datasets with known drug-disease similarities. We propose a machine learning pipeline that uses unsupervised deep embedded clustering, combined with supervised graph neural network link prediction to identify new drug-disease links from multi-omic data. Unsupervised autoencoder and cluster training reduced the dimensionality of omic data into a compressed latent embedding. A total of 9,022 unique drugs were partitioned into 35 clusters with a mean silhouette score of 0.8550. Graph neural networks achieved strong statistical performance, with a prediction accuracy of 0.901, receiver operating characteristic area under the curve of 0.960, and F1-Score of 0.901. A ranked list comprised of 477 per-cluster link probabilities exceeding 99 percent was generated. This study could provide new drug-disease link prospects across unrelated disease domains, while advancing the understanding of machine learning in drug repurposing studies.