This chapter investigates the integration of the Internet of Things (IoT) and deep learning technologies to advance food quality monitoring, enhancing safety and freshness in the supply chain. IoT sensors capture real-time data on environmental conditions such as temperature, humidity, and gas composition throughout food production and supply. Deep learning algorithms analyze this data to detect anomalies and predict potential hazards, enabling proactive interventions. The chapter discusses IoT devices like smart sensors and wearables, and compares deep learning models for predictive analytics and pattern recognition. Case studies highlight reduced spoilage, increased shelf life, and compliance with safety standards. © 2025 Elsevier B.V., All rights reserved.