Breast cancer, a formidable ailment, stands as a prominent contributor to global female mortality rates. The timely detection of breast cancer is of utmost importance as it significantly enhances the probability of a favourable prognosis while concurrently reducing the likelihood of the disease advancing to an incurable state. Artificial Intelligence (AI) and Machine Learning (ML) have emerged as prominent methodologies for the precise detection and classification of breast cancer within Computer-Aided Diagnosis (CAD) systems. This paper provides a comprehensive overview of the existing body of literature pertaining to the application AI in the realm of breast cancer detection. The primary objective of this study is to underscore the significance of employing AI in the timely identification of breast cancer, thereby enhancing the efficacy of subsequent treatment interventions. Furthermore, an examination of different screening methodologies for the detection of breast cancer is presented. Furthermore, we explore the fundamental components of CAD system, including preprocessing, segmentation, feature extraction, and feature selection. This paper will extensively examine the various classification strategies employed in the identification of breast cancer