The growing issue of dietary-related health issues has led to the need for accurate methods to assess food's nutritional content. This research paper presents a model that integrates Artificial Intelligence (AI) and Internet of Things (IoT) technologies to detect and evaluate food nutritional value. The AI component uses deep learning techniques, particularly Convolutional Neural Networks (CNNs), to process images of food items, enabling users to quickly assess their nutritional content. The IoT aspect involves a network of sensors throughout the food supply chain, monitoring key parameters like temperature, humidity, and storage conditions. The collected data is then integrated with AI-generated nutritional analyses, considering environmental factors that may impact food quality. Experiments on a diverse food dataset demonstrated high accuracy in nutritional value prediction, with real-time monitoring through IoT sensors. The system's scalability and adaptability make it suitable for various applications, from individual dietary monitoring to large-scale food production and distribution networks.