Artificial intelligence (AI) is reshaping healthcare, with its applications in transfusion medicine showing great promise to address longstanding challenges. This review explores the integration of AI-driven tools, including Machine Learning (ML), Deep Learning, Natural Language Processing (NLP), and predictive analytics, across various domains of transfusion medicine. From enhancing donor management and optimizing blood product quality to predicting transfusion needs and assessing bleeding risks, AI has demonstrated its potential to improve operational efficiency, patient safety, and resource allocation. Additionally, AI-powered systems enable more accurate blood antigen phenotyping, automate hemovigilance workflows, and streamline inventory management through advanced forecasting models. While these advancements are largely exploratory, early studies highlight the growing importance of AI in improving patient outcomes and advancing precision medicine. However, challenges such as variability in clinical workflows, algorithmic transparency, equitable access, and ethical concerns around data privacy and bias must be addressed to ensure responsible integration. Future directions in this rapidly evolving field include refining AI models for scalability and exploring emerging areas such as federated learning and AI-driven clinical trials. By addressing these challenges, AI has the potential to redefine transfusion medicine, delivering safer, more efficient, and equitable practices worldwide.