A Smart Food Delivery Platform with Machine Learning-Driven Intelligent Matching

This study presents the design and implementation of a smart food delivery platform powered by machine learning-driven intelligent matching. The platform is developed in response to the growing need for efficient and personalized food ordering experiences, reducing the time, effort, and costs associated with visiting physical food outlets. Common issues such as long wait times, lack of real-time expense tracking, and inadequate customer data management are also addressed. The Object-Oriented Analysis and Design Methodology (OOADM) guides the system development, utilizing Android and iOS mobile applications alongside a web interface. JavaScript is employed for interactivity, PHP for server-side processing and database integration, HTML for the user interface, and MySQL for data storage. The system incorporates a machine learning component to analyze user preferences and recommend food options accordingly. The resulting platform streamlines the ordering process, enhances customer satisfaction through intelligent food recommendations, enables real-time tracking of expenses, and improves overall order and customer data management.