Hematology is one of the most data-rich areas of medicine and has consistently been at the forefront of technological innovation. With the increasing integration of machine learning (ML) into the diagnostic process, it is vital that both patient-facing and laboratory-facing members of the care team understand how these tools may interact with existing workflows and affect their work. We review the current landscape of ML research and clinical applications. We cover a wide variety of subdomains (eg, hematopathology, hemoglobinopathies, and coagulopathy) and explore both the success and limitations of corresponding research and deployments.