AI Predictive Analytics for Verifying Pharmaceutical Authenticity and Combating Drug Counterfeiting

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A significant worry in recent years has been the counterfeiting of medicines. The distribution and manufacture of fake or falsified drugs are both criminal and harmful to the public's health. The severity of this issue differs significantly from one country to another, primarily due to variations in the adherence to national regulations and processes. Thus, preventing the sale of fake drugs has become an urgent matter, particularly in poor and developing nations. The study presents a new multi-layered validation structure for pharmaceutical authenticity verification that uses Generative Adversarial Networks (GANs), Convolutional Neural Networks (CNNs) and blockchain technology for artificial intelligence predictive analytics. The goal is to tackle the widespread problem of counterfeit drugs. Using GANs to sift through past data, the suggested strategy improves the detection of minor package variations by identifying trends and traits linked to counterfeit drugs. Using the GAN-generated enhanced dataset, a CNN is trained to accurately and specifically categorize drug packaging.Further, the framework uses blockchain technology to provide trustworthy and transparent recordkeeping of drugs in realtime as they move through the supply chain. Increased patient safety directly results from our system's thorough audit trail, which solves the problems of fake medications and regulatory compliance. The suggested approach reduces counterfeit dangers and provides a scalable paradigm for other industries to use when dealing with similar authenticity verification issues, which means it can find more uses in safety-critical fields. The success of the multi-layered validation structure model is evaluated using performance metrics such as accuracy, recall, and F1-score. It achieves a fantastic accuracy rate of over 95%.