Breast Cancer (BC) remains one of the most prevalent and life-threatening diseases among women worldwide. Early and accurate detection is crucial for effective treatment and for reducing mortality rates. Aim: This study aims to provide a comprehensive review of existing Machine Learning (ML) and Deep Learning (DL) algorithms applied to BC diagnosis and classification, emphasizing their performance, strengths, and limitations. A detailed comparative analysis of various ML and DL techniques used in BC prognosis was conducted. The review covers algorithms applied to diverse data types, including mammograms, histopathological images, Fine Needle Aspiration (FNA) samples, clinical records, and genetic markers. Most algorithms demonstrate high Accuracy (ACC) in differentiating benign from malignant tumors. Techniques leveraging hybrid or ensemble learning approaches show improved diagnostic precision. However, model interpretability, dataset imbalance, and generalization across heterogeneous data sources remain key challenges. The findings highlight the growing role of ML and DL in advancing computer-aided BC diagnosis. Future research should focus on explainable models and data harmonization to enhance clinical trust and real-world applicability.