<p>Breast cancer remains the leading cause of morbidity and mortality for women worldwide. This study focuses on the ability of DNA-based hybrid materials, including DNA-Gold Nano Particles (AuNP) conjugates and DNA nanostructures, to enhance the early detection and diagnosis of breast cancer. By utilizing the emerging technology of DNA nanotechnology, this review identifies improvements in the sensitivity and specificity for the detection of biomarkers critical to breast cancer, such as TP53, BRCA1, BRCA2, PIK3CA, and ESR1. These biomarkers can now be measured through highly developed sequencing technologies combined with polymerase chain reactions. This research also delves into mutation detection, technological methods, and various stages of breast cancer, offering a comprehensive approach to understanding and managing the disease. Complementing these advancements, the proposed HER2Classifier, integrating a ResNet101 backbone with an attention mechanism, demonstrates robust performance in classifying HER2 status using the Zenodo breast cancer dataset. The classifier’s two-step classification strategy effectively addresses class imbalance, enhancing the detection of HER2 High samples. By generating visual attention maps, the model offers interpretability, which is crucial for clinical applications. The integration of mixed precision training and dynamic memory management ensures scalability and memory efficiency, paving the way for potential clinical deployment. The synergy between DNA-based technologies and the HER2Classifier highlights the need for further development to fully harness the potential of these technologies in personalizing care and enhancing outcomes in breast cancer treatment.</p>