Breast cancer constitutes a significant threat to women's health and is considered the second leading cause of their death. Breast cancer is a result of abnormal behavior in the functionality of the normal breast cells. Therefore, breast cells tend to grow uncontrollably, forming a tumor that can be felt like a breast lump. Early diagnosis of breast cancer is proved to reduce the risks of death by providing a better chance of identifying a suitable treatment. Machine learning and artificial intelligence play a key role in healthcare systems by assisting physicians in diagnosing early, better, and treating various diseases. For achieving the early detection of breast cancer, this paper proposes a Machine Learning-based two-level top-down hierarchical approach for breast cancer detection and classification into three classes: normal, benign, and malignant, using the Mammographic Image Analysis Society (MIAS) mammography dataset. Different data preprocessing techniques are applied before using feature extraction techniques and machine learning algorithms for classification. The first classification stage which distinguishes

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