A Review of Blockchain-Based Drug Supply Chain Management Using Contextual White Shark Attention Networks

Abstract

The rapid digital transformation of the pharmaceutical industry has created a growing demand for intelligent technologies that improve transparency, efficiency, and security across drug supply chains. Pharmaceutical supply chains involve multiple stakeholders, including manufacturers, distributors, pharmacies, healthcare providers, and patients, making effective coordination essential. Blockchain technology has emerged as a reliable solution by providing decentralized, tamper-resistant data storage, secure transaction records, and real-time traceability of pharmaceutical products. These capabilities help ensure drug authenticity, minimize counterfeit medicines, and enhance trust among all supply chain participants. Alongside blockchain, artificial intelligence and deep learning have become important tools for improving pharmaceutical decision-making and predictive analytics. Deep learning models analyze large healthcare datasets to support drug demand forecasting, inventory optimization, personalized treatment recommendations, and supply chain management. Optimization algorithms further enhance these models by efficiently tuning network parameters and improving predictive performance. The Contextual White Shark Attention Network, combined with the White Shark Optimizer, represents a promising hybrid framework for learning complex contextual relationships while optimizing model accuracy. This review examines recent developments in blockchain-enabled pharmaceutical supply chains integrated with deep learning and optimization techniques, highlighting current architectures, benefits, challenges, and future research directions for intelligent drug supply chain management and recommendation systems in smart pharmaceutical environments.