The integration of digital and computational pathology into breast pathology practice is evolving the field. Leveraging machine learning, pathologists can augment their workflows in disease classification, biomarker quantification, and outcomes prediction. There are commercial and research applications in digital and computational pathology specific to breast pathology, focusing on diagnostic, predictive, and prognostic tools. Use cases demonstrate improvements in accuracy, productivity, and discovery. Challenges including pre-analytical variability, interoperability, and explainability are discussed alongside emerging solutions and future directions. Digital and computational pathology hold immense promise for standardizing breast cancer diagnosis, optimizing patient management, and discovering novel insights.