Post-expiry blood wastage presents a substantial global healthcare challenge. Blood is an essential and irreplaceable healthcare resource, used for surgeries, emergency care, and chronic illness management. Despite technological breakthroughs, blood cannot be artificially generated and must be donated by volunteer donors, whose availability is intrinsically unpredictable. This variable donor availability, along with the limited shelf life of blood, makes it an essential resource that must be carefully managed – amidst the fluctuating blood demand. However, inefficiencies in demand forecasting, inventory optimization, and communication continue to contribute to substantial discards of this life-saving resource. To address these inefficiencies, the project B-Track introduces a data-driven solution using historical data (2020–2024) from Sri Lanka's National Blood Transfusion Service, supplemented by expert interviews. The proposed solution is a multivariate Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN) model integrated into a platform that supports proactive inventory management. By analyzing complex trends, B-Track provides more accurate demand forecasting, while enabling timely decision-making through automated near-expiry alerts and optimized redistribution mechanisms. This integrated system aligns blood collection with anticipated demand, minimizing expiry-related wastage. Key findings demonstrate that the developed multivariate LSTM forecasting model achieved a demand prediction accuracy of 63.79%, outperforming both SARIMA (42.17%) and Univariate LSTM (50.21%) models. System testing further validated the effectiveness of real-time inventory tracking, alerts, and redistribution protocols. This research contributes a scalable model for enhancing blood bank sustainability by merging machine learning-driven forecasting with dynamic inventory optimization, paving the way for a more efficient and equitable blood supply chain.