Counterfeit medicines absolutely show a high threat to public health and non-functional areas in the pharmaceutical industry that do not have proper regulatory mechanisms in place. Such counterfeit drugs might contain the wrong doses or even some hazardous materials. Hence, they break the trust formed between the healthcare system and patients and expose patients to severe health risks. This project presents a complete solution integrated with blockchain technology and machine learning features to ensure drug authenticity and to protect the pharmaceutical supply chain. Blockchain module built on Hyperledger Fabric gives the tamper-proof, decentralized ledger for medicine logistics tracking. Each medicine has a unique QR code that links together with its whole manufacturingand regulatory information. This provides the scope for customers and employees to check medicines authenticity at the same time just by scanning the code. In other words, this encourages transparency and makes traceability, thus preventing counterfeit drugs entering the supply chain. The protection of the blockchain infrastructure is ensured by employing an anomaly detection model using XGBoost-based machine learning. Trained on the NSL-KDD dataset, the model is capable of identifying and nullifying network malicious activities such as unauthorized access attempt, thereby ensuring reliability and security of the system. By combining these technologies, an all-in-one, scalable solution for minimizing counterfeiting medicines is available. The data within the framework can be kept by blockchain integrity and accessibility while providing machine learning security and thus forming a complete counterfeiting regime. The system not only protects public health but also improves the culture of trust and transparency in the pharmaceutical supply chain, making it a feasible approach for large-scale implementation in the industry.