Traditional drug development is a resource-intensive and time-consuming process with a high rate of failure. To expedite this process, researchers have turned to computational approaches to construct comprehensive graphs of drug-disease associations and explore drug repurposing, finding novel therapeutic applications for existing medications. In parallel, the rapid advancement of the machine-learning field, coupled with the evolution of Natural Language Processing, shows capabilities for reasoning and extracting relationships across various domains. Here, we introduce LOVENet (Large Optimized Vector Embeddings Network), a new framework maximizing the synergistic effects of knowledge graphs and large language models (LLMs) to discover novel therapeutic uses for pre-existing drugs. Specifically, our approach fuses information from pairs of embedding from Llama 2 and heterogeneous knowledge graphs to derive complex relations of drugs and diseases. To empirically validate our methodology, we conducted benchmarking experiments against state-of-the-art algorithms, utilizing three distinct datasets. Our results demonstrate that LOVENet consistently outperforms all other baselines.