Transfusion medicine is vital to healthcare and affects clinical outcomes, patient safety, and system resilience while addressing challenges such as blood shortages, donor variability, and rising costs. The integration of artificial intelligence (AI) and machine learning (ML) presents new opportunities to improve clinical decision-making and operational effectiveness in this field. This structured narrative review identified and evaluated studies applying AI and ML in transfusion medicine. A search of PubMed and Scopus for articles published between January 2018 and April 2025 yielded 565 publications. Studies were included if they applied AI or ML techniques, focused on transfusion management or decision support, and were evaluated using electronic health records or expert review. Four exemplar studies were selected, each representing a distinct AI paradigm: supervised, unsupervised, reinforcement, and generative learning. These studies were critically appraised for methodological rigor, clinical relevance, and potential for implementation in practice. The reviewed studies reflected a clear shift from traditional analytic methods toward more advanced computational approaches to improve prediction accuracy, optimize resource allocation, and support clinical decision-making. Three overarching themes emerged: the need to balance model complexity with interpretability and clinical feasibility; the impact of data quality and preprocessing on model performance and fairness; and the barriers to broader applicability and cross-institutional deployment. As technological barriers continue to decline, future challenges will increasingly center on privacy regulations, infrastructure constraints, and aligning model complexity with practical utility. Thoughtful integration of these considerations through scalable, clinical-grade, and transparent solutions will be critical in realizing the full potential of AI and ML in transfusion medicine.