The pharmaceutical industry faces a growing threat from counterfeit medicines, which endanger patient
safety, public health, and trust in healthcare systems. Traditional methods of authentication, such as
manual inspection, barcode checks, and laboratory chemical analysis, are often slow, expensive, and
not always available at the point of sale. This paper presents a robust framework for pharmaceutical
product authentication and verification of genuine and counterfeit medicine. The proposed framework
integrates image-based inspection, machine learning, rule-based validation, anomaly detection, and
secure product data verification to analyze packaging characteristics, labeling details, and unique
product identifiers. By comparing real-time input data with trusted manufacturer and supply-chain
records, the system supports rapid and practical identification of suspicious medicines. The framework
is designed to be scalable, affordable, and usable by pharmacists, distributors, and consumers. The
overall objective is to improve traceability, reduce counterfeit drug circulation, and strengthen safety in
pharmaceutical distribution environments.
