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This article provides a comprehensive review of the use of machine learning (ML) in transfusion medicine. It discusses the potential benefits of ML in improving patient outcomes and clinical practice in patient blood management. The review covers various studies that have developed prediction models for blood transfusion requirements in different surgical procedures, as well as studies on predicting transfusion needs in trauma patients, identifying risk factors for adverse outcomes, assessing transfusion appropriateness, and investigating transfusion safety. The studies utilize different ML models and report varying levels of accuracy in their predictions. The article highlights the potential of ML in improving transfusion practices and optimizing inventory management in hospital settings, but also emphasizes the need for further research, validation, and standardization in this field. [Extracted from the article]