Graph theory has emerged as a powerful tool in understanding the complex structure and dynamics of biological networks, providing valuable insights into the study of diseases and epidemics. Biological systems, such as gene regulatory networks, protein-protein interaction networks, and the spread of infectious diseases, can be represented as graphs, where nodes correspond to biological entities (genes, proteins, individuals) and edges represent relationships or interactions. This framework allows for the exploration of key phenomena such as network robustness, contagion dynamics, and the identification of critical nodes (hubs) that may influence disease progression. In the context of epidemic modeling, graph theory enables the simulation and analysis of disease transmission patterns across populations, offering strategies for containment and prevention. The integration of graph-based models with epidemiological and molecular data has led to the development of more accurate and efficient models for predicting the spread of infectious diseases, including viral outbreaks.