To understand a disease, the need to find out which genes are involved in causing it is crucial. Identifying and linking genes to diseases is difficult and costly to conduct experiments involving a huge pool of potential candidate genes. As a result, alternative computational methods that are both cost-effective and easy have been introduced for identifying candidate genes linked to specific diseases. Because genes causing the same or similar diseases have less variance in their sequence or network features of protein-protein interactions, the majority of these strategies rely on phenotypic similarities. This is based on the idea that genes are located closer together in the protein interaction network that causes the same or similar disease. Nevertheless, these techniques solely rely on fundamental network properties, topological features, gene sequences, or existing biological knowledge as a preliminary, limiting the identification process to individual gene-disease associations. The introduction of innovative computer-driven approaches to discover genes that play a role in various diseases is done. These approaches can be used as tools that use computational power to help understand which specific genes might be connected to different health conditions.