Abstract: Automating nutritional content analysis of food products is critical in promoting healthier eating habits and ensuring food safety. This research paper explores the integration of image recognition and deep learning technologies to develop an automated system for assessing the nutritional content of food products. By employing convolutional neural networks (CNNs) and advanced image processing techniques, the proposed system can accurately identify food items and estimate their nutritional value based on visual attributes. The paper discusses the architecture of the deep learning model, data preprocessing methods, and the integration of nutritional databases for precise content analysis. The study also evaluates the system's performance in real-world scenarios, highlighting its potential for use in both consumer-facing applications and industrial food processing environments. The findings demonstrate that image recognition combined with deep learning provides a robust solution for automating nutritional content analysis, offering scalability and efficiency compared to traditional methods.