An AI-Driven Blood Bank Donation and Management System: An Integrated Framework for Matching, Prediction, Anomaly Detection, and Inventory Optimization

Blood banks are a critical part of modern health- care, yet many still rely on manual workflows and reactive decision-making. Such systems struggle to handle fluctuating demand, limited shelf life of blood components, and the urgency of emergency cases. These limitations often lead to shortages, un- necessary wastage, delays in fulfillment, and mismatches between donors and recipients. This paper presents a unified AI-driven Blood Bank Donation and Management System designed to ad- dress these challenges through intelligent donor–recipient match- ing, donor eligibility prediction, inventory forecasting, emergency demand prediction, routing optimization, and fraud and anomaly detection. The proposed framework combines machine learning, deep learning, and statistical techniques to support end- to-end blood bank operations. In addition, the system includes an AI- assisted documentation module to help students and healthcare professionals generate structured research reports and technical documentation. Experimental simulations demonstrate improved matching accuracy, reduced wastage, and faster emergency re- sponse, highlighting how AI can transform traditional blood bank systems into proactive, data-driven healthcare infrastructure.