Breast cancer continues to be a major cause of death among women worldwide, with early diagnosis being essential for improving survival rates. In South Africa, the success of mammography screening is hampered by healthcare inequalities, a lack of radiologists, and infrastructural issues. This review explores recent progress in machine learning (ML) for the early detection of breast cancer through mammography images, drawing from peer-reviewed sources indexed in IEEE, Research Gate, Springer, Scopus, and Google Scholar. The focus was on model performance across various datasets, preprocessing techniques, and methods for feature selection. The findings from the studies reviewed suggest that ML models employing techniques such as the synthetic minority oversampling technique (SMOTE) and Recursive Feature Elimination (RFE) can attain high accuracy (exceeding 90%), enhance diagnostic reliability, and provide uncomplicated, cost-effective solutions suitable for environments with limited resources. Utilizing the Radial Basis Function (RBF) kernel facilitated better management of non-linear data patterns frequently seen in medical imaging. The findings of this review paper show that machine learning, especially SVM and CNN, show an impressive accuracy and precision, thus achieving an outstanding performance compared to other ML methods. AI offers significant benefits that include enhanced diagnostic precision, efficiency, and personalized screening, while reducing radiologists’ workload. Nonetheless, challenges such as biased datasets, high implementation costs, and unresolved legal and ethical issues hinder their widespread clinical adoption. In summary, the outcome indicates that AI-powered methods could significantly enhance the early detection of breast cancer. However, for these methods to be used in clinical settings, challenges like transparency, diversity of datasets, and how they fit into the current healthcare practices need to be addressed.