Food wastage is a global issue, with vast amounts of edible food discarded annually while millions face hunger. This research proposes a Waste Food Management and Donation Application to connect food donors, such as restaurants, event organizers, and individuals, with NGOs, food banks, and charities. The platform facilitates efficient redistribution of surplus food to those in need, minimizing food waste and reducing landfill burden. The platform’s backend is powered by PHP and the Laravel framework, ensuring secure and efficient operations. For database management, MySQL is used to maintain reliability. On the frontend, technologies such as HTML, CSS, and JavaScript work together to create an accessible and easy-to-use interface, featuring tools like live tracking of donations and Google Maps integration for location tagging. The application incorporates machine learning techniques to forecast regions with higher demand, streamline the logistics of donations, and identify irregularities in food collection processes. Empirical results indicate a 30% reduction in transportation costs and a 25% improvement in donation efficiency based on initial testing with local food banks. The system also provides real-time insights into donation trends, helping to optimize redistribution. This innovative solution integrates supervised and unsupervised learning models for effective resource allocation and clustering donation behaviors. In conclusion, by leveraging technology, the platform effectively addresses hunger and waste, creating a scalable, data-driven system that promotes social welfare and environmental sustainability.