Breast cancer is one of the most common health concerns among women, worldwide. It is, therefore, important to accurately diagnose the issue to improve the outcome of the patient. Recent advancement in deep learning techniques promises to detect and classify breast cancer through analysis of medical images. In this study, we have investigated and compared the performance of “You Look Only Once” (YOLO) models for breast cancer classification, specifically YOLOv8 and YOLOv11. These models are evaluated to determine their effectiveness in accurately identifying breast cancer cases from medical images. The study compares these models on the basis of some key parameters like—overall accuracy, precision, recall, and computational efficiency. The parameters help us to understand the benefits and drawbacks of each of the models studied. We have also discussed the underlying architectures of these models and their design choices to understand how it affects their performance in breast cancer classification task. The results suggested that each model presents its own advantage on the basis of application requirement. In this paper, we aim to provide valuable insights for researchers and healthcare providers to practice deep learning methods for breast cancer diagnosis. Finally, we aim to contribute an insight into the usage of specific method or a set of methods for achieving better outcomes in different scenarios.