Breast cancer is a major global health issue for women. Timely detection and precise classification are important in effective treatment and enhancing chances of survival. Historically, experts have used mammography and MRI in classification, which depend on expert interpretation and vary in accuracy. This study investigates the use of deep learning models, VGG16 and EfficientNet B7 in particular, to classify breast cancer images. These models focus on three classes: benign, malignant, and normal based on Breast Ultrasound Images Dataset (BUSI). The report identifies how these models revolutionize medical diagnostics. It analyzes the tremendous significance of transfer learning along with fine-tuning toward improving accuracy while reducing overfitting tendencies. The findings from this research indicate that model selection must be specific to medical imaging tasks.