UNLABELLED: Breast cancer is a common cancer type that occurs among women in Pakistan, and the rising incidence and mortality rate underline the need to develop effective, patient-tailored immunotherapies. In this study, we implemented an end-to-end immunoinformatics workflow using publicly available whole-exome sequencing (WES) data deposited under NCBI BioProject PRJNA941166, a cohort-derived resource from the Khyber Pakhtunkhwa region; as a proof of concept, we analyzed all sequencing runs associated with the available case to demonstrate a personalized vaccine design workflow. Somatic variant analysis indicated a high mutational burden, including 6005 missense mutations in genes such as MUC3A and TTN. From > 43,000 candidate mutant peptides, we prioritized seven non-allergenic neoantigens with strong predicted HLA binding (ΔG ≤ - 13.0 kcal/mol). These epitopes were assembled into a 285-amino acid multi-epitope antigen incorporating a GM-CSF adjuvant and helper epitopes. AlphaFold2 modeling and in silico quality assessment supported construct stability (ProSA Z-score - 7.14; ERRAT 96.59%). Across 500 ns molecular dynamics simulations, the vaccine construct remained conformationally stable and showed favorable predicted interactions with innate immune receptors, with strong binding free energies for TLR9 (ΔG = - 148.8 kcal/mol) and TLR2 (ΔG = - 16.7 kcal/mol). Immune simulations using C-IMMSIM suggested a Th1-skewed response characterized by induction of cytotoxic T lymphocytes, memory T-cell formation, and elevated IFN-γ. Although limited to computational predictions and a single publicly available case, the predicted receptor engagement and immunogenicity provide a rationale for preclinical evaluation of this personalized mRNA vaccine design workflow in high-risk populations. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s40203-026-00631-6.