According to the World Health Organization’s statistical data for 2024, breast cancer is the most often diagnosed cancer among women. Between 2020 and 2024, approximately 37,030 new instances of invasive breast cancer were documented in women. Recent advancements in deep learning have shown considerable potential to improve the accuracy of breast cancer diagnosis, ultimately aiding radiologists and clinicians in making more precise decisions. This study presents a strategy that creates a highly dependable ultrasound analysis reading system by comparing the powerful processing capabilities of CNNs with four pre-trained models (Transfer Learners). The models employed were the DenseNet 169, ResNet 152, MobileNet V2, and Xception. To assess the effectiveness of the proposed framework, experiments were conducted using established benchmark datasets (BUSI datasets). The suggested framework has demonstrated superior performance compared to previous deep learning architectures in precisely identifying and categorizing breast cancers in ultrasound images. Upon comparison of the specified deep learning models, DenseNet 169 had the maximum performance with an accuracy of 99.7%. This surpasses the research undertaken in the literature. This research employs advanced deep learning algorithms to enhance breast cancer diagnostic outcomes, decreasing diagnosis time and facilitating early treatment.