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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM63600

AI-Driven Blood Donation And Crisis Response Network Using Geo Tracking

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM63600

Volume:

10 |

5 |

May 2026

Pages:

1-4

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
Not assigned

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.

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Authors

KP

Kadambari Pardeshi

Computer

Matoshri College Of Engineering And Research Centre, Nashik

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SS

Sakshi Sanap

Computer

Matoshri College Of Engineering And Research Centre, Nashik

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NM

Neha More

Computer

Matoshri College Of Engineering And Research Centre, Nashik

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SU

Sakshi Ugale

Computer

Matoshri College Of Engineering And Research Centre, Nashik

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Abstract

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

Keywords- Smart Blood Management Emergency Healthcare System Predictive Analytics GIS Tracking AI in Healthcare Blood Bank Automation Donor Alert System

Cite This Article

Kadambari Pardeshi, Sakshi Sanap, Neha More, Sakshi Ugale, "AI-Driven Blood Donation And Crisis Response Network Using Geo Tracking," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 5, pp. 1-4, May 2026.

Full Article/Conclusion

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.

References

[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.

Editorial & Review Transparency

Publication Date

5/20/2026

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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