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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM63600
Volume:
10 |
5 |
May 2026
Pages:
1-4
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Computer
Matoshri College Of Engineering And Research Centre, Nashik
Computer
Matoshri College Of Engineering And Research Centre, Nashik
Computer
Matoshri College Of Engineering And Research Centre, Nashik
Computer
Matoshri College Of Engineering And Research Centre, Nashik
Blood availability during emergencies and natural disasters remains one of the major challenges faced by healthcare organizations. Existing blood donation systems often lack instant communication, intelligent forecasting, and efficient donor coordination. This research proposes a Smart Emergency Blood Supply Management System that combines artificial intelligence, geo-location services, and predictive analytics to improve blood availability during critical situations. The system enables hospitals and blood banks to send emergency alerts to nearby eligible donors using real-time GPS tracking. Machine learning algorithms analyze previous donation records, seasonal demand patterns, and emergency data to predict future blood shortages. The proposed framework also supports optimized donor matching, emergency response coordination, and secure data handling through cloud infrastructure .The developed system aims to reduce response time, improve blood inventory management, and enhance healthcare preparedness during disasters and medical emergencies.
KEYWORDS
The AI-Driven Blood Donation and Crisis Response Network using Geo Tracking provides an intelligent and efficient solution for emergency blood management. The integration of Artificial Intelligence, Machine Learning, GIS, and real-time geo-tracking technologies enhances donor identification, blood shortage prediction, and emergency communication. The system enables rapid response during disasters by connecting hospitals, blood banks, and donors through a centralized smart platform. The proposed framework improves healthcare resilience, reduces delays in blood delivery, minimizes shortages, and supports life-saving decisions during critical situations. Future enhancements may include blockchain-based security, IoT-enabled health monitoring, and advanced deep learning models for more accurate prediction and disaster management.
[1] S. Bhattacharya, A. Sharma, and R. Singh, “AI Powered Blood Donation Management System for Disaster Response,” IEEE International Conference on Smart Technologies, 2024. [2] N. Gupta, P. Srivastava, and T. Mehta, “Blockchain Enabled Secure Blood Donation Network for Emergency Scenarios,” IEEE Access, 2023. [3] R. Kumar and S. Kaur, “Smart Blood Bank Management System using Cloud and GPS Technologies,” IJACSA, 2024. [4] L. Zhang, Y. Liu, and C. Wang, “Machine Learning Based Prediction of Blood Demand in Disaster Situations,” IEEE GHTC Conference, 2023. [5] A. Verma and S. Joshi, “Integration of GIS and AI for Real-Time Disaster-Aware Blood Distribution,” Journal of Geographic Information Systems, 2024. [6] T. Nguyen and H. Lee, “AI and Data Analytics for Optimizing Healthcare Resource Allocation during Disasters,” IEEE Transactions on Computational Social Systems, 2024. [7] P. Chakraborty and S. Banerjee, “Predictive Modeling of Blood Donation Needs Using Deep Learning,” Springer Applied Sciences, 2023.
Publication Date
5/20/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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No funding information available for this research.
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