The paper provides an innovative model that modernizes food product quality control and traceability by integrating cutting-edge technologies like blockchain, artificial intelligence (AI), machine learning (ML), and the Internet of Things (IoT). Every product has a unique QR code created during the manufacturing process, which enables smooth tracking of the product by all supply chain participants, including suppliers, dealers, manufacturers, customers, and other stakeholders. IoT sensors gather extensive product data, which is then sent over the MQTT protocol and examined by Google Vision Inspection AI and a customized TensorFlow deep learning model. To guarantee accurate and reliable quality control, the TensorFlow model is rigorously trained using the VGG19 architecture on the “PepsiCo-Lab-Potato-Quality-Control” dataset. It is then further assessed against a variety of machine learning models. The paper provides a full explanation of the theoretical framework and the possible applications of the system. It also describes how blockchain technology, as used by the Ethereum Foundation’s smart contracts, keeps an unchangeable record of data, improving the supply chain’s security and transparency. Through a straightforward QR code scan, the model enables customers to evaluate the quality and history of the food they have purchased, guaranteeing a high degree of quality assurance and boosting confidence in the food supply chain. With its goal of establishing a new benchmark for the sector, the suggested model highlights the revolutionary power of AI and ML in attaining hitherto unheard-of levels of traceability and quality control in food supply chains.