Breast cancer continues to be one of the most prevalent and life-threatening diseases affecting women worldwide. Early and accurate diagnosis significantly improves survival rates, and in recent years, machine learning (ML) has emerged as a transformative tool in enhancing diagnostic precision. This paper presents a comprehensive state-of-the-art review of publicly available machine learning datasets specifically designed for breast cancer detection. The review categorizes and analyzes a wide range of datasets including tabular, imaging, and genomic types such as the Wisconsin Breast Cancer Dataset (WBCD/WDBC), BreakHis, Digital Database for Screening Mammography (DDSM), MIAS, CBIS-DDSM, and TCGA-BRCA. Each dataset is evaluated based on key parameters such as data type, feature richness, class balance, sample size, and its suitability for various ML tasks like classification, segmentation, and multimodal learning. Additionally, the paper outlines the strengths, limitations, and real-world applicability of each dataset, providing critical insights for researchers in selecting appropriate benchmarks for model development. The study also highlights current challenges and suggests future directions for constructing more diverse, annotated, and standardized datasets to support robust and generalizable breast cancer detection systems.