Breast cancer continues to be a primary cause of death for women globally, making early identification crucial for enhancing survival outcomes. This review article synthesizes recent advancements in machine learning (ML) applications for breast cancer detection, focusing on a seminal study that employs interpretable ML models on genomic data from the METABRIC dataset. We explore the epidemiology, symptoms, causes, and diagnostic challenges of breast cancer, followed by an in-depth analysis of ML methodologies including data preprocessing, feature selection, oversampling, classification algorithms (Random Forest, Logistic Regression, K-Nearest Neighbors, Support Vector Machines), ensemble learning, and interpretability via SHAP. Random Forest achieves superior results across various performance indicators such as accuracy, precision, recall, and AUC. The assessment addresses the work's contributions, shortcomings, and potential developments, including expanding data resources and merging with contemporary clinical systems. By emphasizing transparency in ML for healthcare, this article underscores the potential of these techniques to support clinicians in decision-making, ultimately aiming to reduce the global burden of breast cancer.