Breast cancer is one of the most common cancers among women, and early detection plays a critical role in improving treatment outcomes and reducing mortality. Mammography, ultrasonography, magnetic resonance imaging, and biopsy remain the main methods used in breast cancer screening and diagnosis. However, dense breast tissue, difficulties in image interpretation, false-positive findings, unnecessary biopsies, and increased radiologist workload may limit diagnostic accuracy and efficiency. In recent years, artificial intelligence, machine learning, deep learning, and radiomics have emerged as promising technologies to address these limitations. AI-supported systems can analyze large datasets obtained from medical images and contribute to the detection of suspicious lesions, prediction of breast cancer risk, classification of tumor characteristics, and support of clinical decision-making processes. Radiomics enables the extraction of quantitative imaging features and their integration with clinical and biological data, providing valuable information about prognosis, treatment response, and individualized disease management. Nevertheless, the safe integration of AI into clinical practice requires algorithm validation across different populations, high-quality data, transparency, ethical considerations, and protection of patient privacy. Artificial intelligence should not be regarded as a replacement for healthcare professionals, but rather as a complementary technology that supports diagnostic accuracy, reduces workload, and strengthens clinical decision-making. With multidisciplinary collaboration and robust clinical validation, AI-based applications are expected to become increasingly important in breast cancer screening and diagnosis.