ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL MANUFACTURING AND QUALITY CONTROL: A COMPREHENSIVE REVIEW

Abstract

Artificial Intelligence (AI) is rapidly transforming pharmaceutical manufacturing and quality control by enhancing efficiency, accuracy, and regulatory compliance. The integration of AI technologies, including machine learning, deep learning, and data analytics, enables real-time monitoring, predictive maintenance, and optimization of manufacturing processes. This review provides a comprehensive analysis of the applications of AI in pharmaceutical production, focusing on process optimization, fault detection, and continuous manufacturing systems. Additionally, the role of AI in quality control is examined, particularly in areas such as automated inspection, anomaly detection, data integrity, and quality assurance. The implementation of AI-driven tools improves decision-making by analysing large datasets generated during drug development and production. Furthermore, AI facilitates adherence to regulatory frameworks such as Good Manufacturing Practices (GMP) by ensuring consistency and reducing human error. Despite these advantages, challenges including data security, lack of standardization, high implementation costs, and regulatory uncertainties remain significant barriers to widespread adoption. Recent advancements, including the integration of AI with Industry 4.0 technologies such as the Internet of Things (IoT) and digital twins, are also discussed to highlight future perspectives. Overall, AI holds immense potential to revolutionize pharmaceutical manufacturing and quality control, offering improved productivity, product quality, and patient safety. Continued research and regulatory harmonization are essential to fully realize its benefits in the pharmaceutical industry.