Deep learning and convolutional neural networks (CNNs) are transforming breast cancer diagnosis by automating the analysis of histopathological images. Researchers developed a workflow that integrates image preprocessing, hematoxylin–eosin stain separation, K-means clustering, and CNN-based classification to enhance diagnostic accuracy. By filtering poor-quality images and focusing on tumor-rich regions, the model achieved strong classification performance, with 79.0% Top-1 accuracy and 94.5% Top-5 accuracy. Thus, potential use of AI is able to reduce diagnostic variability, support pathologists in clinical decision-making, streamline pathology workflows, and improve access to reliable breast cancer diagnosis, particularly in resource-limited healthcare settings.