Reverse vaccinology is a computational approach for identifying novel vaccine antigens from pathogen genomes without the need for culture, enabling the prediction of potential immunogenic proteins. By employing bioinformatics, molecular simulations, and 3D modeling, this strategy facilitates the design of multi-epitope and subunit vaccines that elicit stronger immune responses and broader protection. In cancers such as lung, colorectal, breast, pancreatic, and liver malignancies, reverse vaccinology enables the identification of tumor-specific neoantigens and mutations, leading to the development of personalized vaccines. These vaccines have demonstrated the ability to activate T and B cell responses, enhance the production of antitumor cytokines, and inhibit tumor growth. The use of nanocarriers, adjuvants, and specific linkers further improves vaccine efficacy and safety. Molecular docking(It is a computational technique for predicting how a molecule binds to a target site) and simulations with TLR and HLA receptors have confirmed the stability and effectiveness of the designed vaccines. The advantages of this approach include personalized therapy, induction of long-lasting immunological memory, and compatibility with other treatment modalities. However, challenges remain, including optimal epitope selection, accurate prediction of peptide–MHC interactions, and overcoming tumor-induced immune suppression. Integration of machine learning, molecular dynamics, and advanced delivery systems has been proposed as promising strategies to optimize vaccine performance. With ongoing progress in omics sciences and bioinformatics, reverse vaccinology is expected to become a key tool for developing safe, effective, and personalized cancer vaccines, potentially transforming cancer immunotherapy by 2030.