Breast cancer is the most common malignancy among women worldwide and, owing to its marked genetic heterogeneity, presents substantial challenges for accurate risk stratification and optimal treatment selection. This narrative review with an analytical perspective aims to evaluate the clinical applications of Whole Exome Sequencing (WES), explore its associated challenges, and discuss future directions in breast cancer management. Relevant studies published up to 2026 were identified through systematic searches of the PubMed and Embase databases, and articles addressing the clinical utility of WES in breast cancer were included. Current evidence indicates that WES, by targeting the protein-coding regions of the genome, enables the simultaneous detection of both established and novel genetic variants. Compared with targeted gene panels, WES offers more comprehensive coverage of clinically relevant genomic alterations, while remaining more cost-effective and clinically feasible than whole-genome sequencing (WGS). WES has emerged as a valuable tool for characterizing tumour heterogeneity, assessing tumour mutational burden (TMB), identifying actionable genetic alterations, and supporting patient selection for immunotherapy and precision oncology approaches. Despite these advantages, several challenges continue to hinder the widespread clinical implementation of WES, including difficulties in interpreting variants of uncertain significance (VUS), technical and analytical variability, limited genomic data from underrepresented populations, and the financial and infrastructural requirements associated with large-scale sequencing. Overall, WES represents a powerful genomic technology with considerable potential to improve the diagnosis, prognostic assessment, and personalized treatment of breast cancer. As sequencing technologies and bioinformatics methodologies continue to advance, its role in routine clinical practice is expected to expand. However, the full realization of its clinical utility will depend on greater standardization, improved representation of diverse populations in genomic databases, and the development of robust frameworks for clinical interpretation and decision-making.