The rapid increase in urban poverty, food wastage, and textile waste has created a significant need for intelligent donation management systems. Traditional donation platforms often suffer from lack of transparency, inefficient resource allocation, delayed deliveries, and poor communication between donors, NGOs, and beneficiaries. This research proposes an AI-driven Smart Donation and Resource Management System that automates the collection, classification, verification, and distribution of food and clothing donations.

The proposed system integrates Artificial Intelligence (AI), Machine Learning (ML), Computer Vision, and Cloud Computing technologies to optimize donation workflows. Convolutional Neural Networks (CNNs) are used for cloth damage detection and quality assessment, while predictive algorithms analyze food freshness and suitability based on environmental and user-provided parameters. The system also provides real-time tracking, volunteer management, donor reward mechanisms, and intelligent location-based distribution.

The research highlights how AI can improve transparency, reduce waste, increase operational efficiency, and support sustainable social welfare activities. The system further promotes employment opportunities through cloth recycling and small-scale tailoring support. Future

enhancements include Blockchain integration, smart logistics optimization, and real-time analytics dashboards.