Post-harvest diseases in mangoes significantly impact their quality, market value, and supply chain efficiency. Identifying and tracking defective mangoes caused by these diseases is essential for reducing losses and ensuring food safety. This study presents the development of a database system designed to record, monitor, and analyse defective mangoes affected by post-harvest diseases such as anthracnose, stem-end rot, and soft rot. The database incorporates key features, including disease classification, severity levels, visual symptoms, geographical origins, storage conditions, and time since harvest. Advanced query functionalities allow stakeholderssuch as farmers, distributors, and researchersto access actionable insights and trends, enabling better decision-making in disease management, transportation, and storage protocols. By integrating data visualization and predictive analytics, the database facilitates early detection of disease patterns, contributing to improved post-harvest handling practices. This initiative aims to enhance the efficiency of mango supply chains, minimize waste, and ensure the delivery of high-quality mangoes to consumers while promoting sustainable agricultural practices.