Urbanization and population growth have placed unprecedented demands on urban food distribution systems, necessitating innovative solutions to ensure efficient, sustainable, and customer-centric operations. This abstract provides a concise overview of the transformative potential of integrating Internet of Things (IoT) and Machine Learning (ML) technologies into urban food distribution systems. IoT sensors offer real-time monitoring capabilities, enabling the continuous tracking of environmental conditions and product quality during food transportation. ML algorithms harness this data to optimize inventory management, forecast demand, and streamline delivery routes, reducing operational costs and minimizing food waste. Quality control and safety are enhanced through computer vision systems, which detect defects and spoilage in food products. The integration of blockchain technology and IoT ensures secure traceability, fostering transparency and accountability throughout the supply chain. Energy efficiency measures, driven by IoT-connected energy management systems and ML, lower carbon emissions and operational costs. Additionally, ML-driven customer analytics enable personalized recommendations and promotions, fostering loyalty and trust. However, adopting IoT and ML in urban food distribution presents challenges, including data privacy, security concerns, and technology integration complexities. Collaborating with stakeholders is vital for success in this evolving landscape. In conclusion, the integration of IoT and ML technologies into urban food distribution systems signifies a paradigm shift towards greater efficiency, sustainability, and customer satisfaction. While challenges persist, the promise of a more responsive and eco-friendly food distribution system underscores the transformative impact of these technologies in addressing urban food supply challenges