In information extraction, not only extracting the entities but also identifying the relationships between the entities can lead to better-hidden insights. Triples are used to represent the relationships between clinical entities. Triples are made up of two concepts and a relationship between them: <Concept1, Relation, Concept2>. Graph data is prominently used to extract vital hidden insights by node-edge analysis. This research studies different graph embedding algorithms to predict the relationship between nodes. The paper examined the state-of-the-art in clinical relation prediction and created a graph-based model to predict the relationship between two clinical concepts. The relation prediction model is trained using the proprietary Graph dataset, maintaining the concept and relation triples. The work implements and compares the results of link prediction algorithms in the clinical domain. The ComplEx graph-based node embedding algorithm has been found to be promising in the prediction of semantically enhanced clinical relationships. The paper discussed the potential use cases of clinical relationship prediction in detail. This research will be critical in determining the causal relationships between medical concepts.