Integrating Machine Learning with Blockchain for Predictive Detection of Counterfeit Pharmaceuticals: A Synthesis of Emerging Frameworks and Applications

Abstract

The proliferation of counterfeit pharmaceuticals poses a significant threat to global health systems, necessitating advanced technological interventions for secure and predictive supply chain management. This study presents a systematic synthesis of emerging frameworks integrating machine learning and blockchain for counterfeit drug detection. Blockchain provides immutable, decentralized data infrastructure, while machine learning enables predictive analytics for anomaly detection within verified transaction datasets. Evidence from selected studies indicates moderate effectiveness in enhancing supply chain transparency and fraud detection, with one reported system achieving 93.31% accuracy using ensemble learning models. However, the analysis reveals a critical lack of standardized evaluation metrics such as precision, recall, and F1-score across implementations. The review further identifies key architectural patterns, including IoT-enabled data acquisition, smart contract automation, and hybrid analytics pipelines. Despite promising conceptual advancements, significant challenges persist in scalability, interoperability, and regulatory compliance. This study contributes a structured evaluation of integration strategies and highlights research gaps in empirical validation and deployment in resource-constrained environments. Future research directions include the adoption of explainable AI models, federated learning for privacy-preserving analytics, and cross-border regulatory frameworks to support large-scale implementation. The findings provide actionable insights for researchers, policymakers, and industry stakeholders seeking to enhance pharmaceutical supply chain integrity and patient safety through intelligent digital infrastructures.