<p>Cancer is a leading cause of death worldwide. Efforts to combat cancer involve a multidisciplinary approach, including healthcare providers, researchers, policymakers, and the public. While cancer remains a significant health challenge, ongoing research and awareness initiatives offer hope for better prevention, earlier detection, and more effective treatments in the future. Breast cancer is indeed a highly significant and concerning global health issue and is considered as the second most dangerous cancer in the world. This review paper includes a thorough discussion of the classification of cancer modalities using machine learning algorithms. This review provides a comprehensive examination of breast cancer classification techniques using machine learning (ML) algorithms, comparing the performance of various methods and highlighting how accuracy varies across datasets, tools, and experimental conditions. Many researchers have put their efforts on breast cancer diagnoses and prognoses, and every technique has a different accuracy rate that varies depending on the situation, tools, and datasets used. Recent technological advancements in ML have fueled the growth of automated breast cancer diagnosis. In addition to the technical review, this study also includes a bibliometric analysis to map publication trends, influential authors, key journals, and emerging research themes in the field, thereby offering a broader perspective on current scientific progress. Additionally, this paper gives all the information a beginner needs to understand machine learning algorithms and build a solid foundation for deep learning. It is imperative to commence with a comprehensive survey of the existing ML methodologies and their hybrid variants, facilitating informed comparisons and case studies for future developments.</p>