Breast cancer is one of the most dangerous diseases and the second largest cause of women cancer death. Techniques and methods have been adopted for early indications of the disease signs as it’s the only effective way of managing breast cancer in women. This review explores the techniques used for breast cancer in Computer-Aided Diagnosis (CAD) using image analysis, deep learning and traditional machine learning. It primarily gives an introduction to the various strategies of machine learning, followed by an explanation of the various deep learning techniques and particular architectures for breast cancer detection and their classification. After the review, the researcher recommended the need for the inclusion of deep learning in machine learning because it performs multi-functions in enabling medical diagnosis. 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