AI-Based Smart Blood Management System: A Comprehensive Review of Data Sources, Machine Learning Methods, and IoT Integration

Blood cannot be manufactured, has a short shelf life and must be available at the right place and time, yet many blood banks still rely on manual records, telephone-based donor mobilisation and rule-of-thumb stock control, which contribute to shortages, expiry wastage and slow emergency response. Artificial intelligence (AI), combined with digital records, Internet of Things (IoT) sensing and blockchain-based traceability, is increasingly proposed to forecast demand, predict donor behaviour and optimise inventory. This paper presents a focused narrative review of peer-reviewed studies on AI in blood management, covering demand forecasting, donor prediction, inventory ordering and traceability. Three connected themes are examined: data sources and sensing technologies, learning methods (time-series models, classical machine learning, gradient boosting and deep learning), and IoT, cloud and blockchain integration. The literature reports that a hybrid forecasting and ordering strategy reduced hospital red-cell inventory by about 40 % and orders by 60 % against historical practice, that an integrated platform increased collected blood by 11 % and reduced wastage by 20 % in a pilot at one centre, and that random forests can identify returning donors with a Matthews correlation coefficient of 0.851. Simple time-series models often remain competitive with deep learning. However, most studies use single-centre or single-country data, retrospective validation and one blood component at a time, and few report multi-site field trials, complete blood-group coverage or long-term IoT operation. The review contributes a comparative analysis of representative studies, a proposed conceptual layered architecture that links data acquisition, edge and cloud AI, blockchain traceability and independent validation, and a prioritised agenda of research gaps and future directions.