Blockchain and Machine Learning for Predictive Resource Distribution in Disaster Response Management

Many disaster response plans depend on timely resource delivery. Traditional systems struggle to allocate resources, collaborate, and foresee resource needs. This paper introduces the Dynamic Resource Allocation Framework (DRAF), a blockchain-machine learning solution to these issues. DRAF demands increased transparency, accountability, and speed in crisis response. This article covers DRAF's three primary components: forecasting resource requirements (RNP), optimizing resource usage (ORA), and tracking and managing resources. They all conduct crucial resource distribution tasks. DRAF is superior at distributing resources effectively, monitoring and coordinating resources in real time, and creating accurate resource estimates. Using current and previous data, machine learning algorithms, and blockchain technology allows for flexible and fast catastrophe resource management. Blockchain's responsibility and openness reduce poor management and cheating while ensuring everyone gets the same amount. Machine learning technologies speed up catastrophe response, reducing casualties. The study found that DRAF might improve emergency response management by simplifying resource planning. As natural and man-made disasters increase, resource management is crucial. DRAF might improve disaster response by making it simpler to support the most vulnerable.