Counterfeiting is a pervasive issue that impacts global supply chains, leading to economic losses, brand dilution, and risks to consumer safety. Existing counterfeit detection methods often lack scalability, accuracy, and security, making it difficult to efficiently track and authenticate products. This research presents a hybrid approach that leverages blockchain technology and Deep Convolutional Neural Networks (DCNN) to enhance counterfeit detection and supply chain transparency. Blockchain technology offers a decentralized and immutable ledger system, ensuring secure and tamper-proof storage of product metadata. By recording critical supply chain events on a blockchain, manufacturers, distributors, and consumers can verify product authenticity in real time. This transparency helps reduce fraud, prevent counterfeit infiltration, and establish a trustworthy ecosystem for various industries. Complementing blockchain’s secure storage, DCNN plays a crucial role in counterfeit detection by analyzing structured data and visual inputs. Deep learning models, particularly DCNN, have demonstrated exceptional performance in pattern recognition and anomaly detection. By training a DCNN model on product metadata, QR codes, packaging images, and other identifiers, the system can detect counterfeit patterns with high accuracy. This integration enables real-time monitoring, providing alerts when anomalies or inconsistencies are detected in supply chain records. The proposed solution is designed to be robust, scalable, and applicable across multiple industries, including pharmaceuticals, electronics, food products, and luxury goods. This research aims to develop a blockchain-based system that securely records and verifies supply chain metadata, ensuring transparency and immutability. Additionally, it focuses on training a DCNN model to analyze structured metadata and visual cues, enabling accurate counterfeit detection. Finally, the study seeks to create a unified framework that seamlessly integrates blockchain and DCNN to facilitate real-time counterfeit prevention and authentication. By combining the transparency and security of blockchain with the advanced detection capabilities of deep learning, this research aims to establish an innovative and effective approach to combating counterfeiting in global supply chains. Overall, this research attempts to bridge the gap between blockchain and AI by combining their strengths to address counterfeit detection challenges. The proposed solution leverages the transparency of blockchain and the advanced analytical capabilities of DCNN to deliver an efficient, secure, and scalable framework for modern supply chains.