Artificial Intelligence in Pharmaceutical Manufacturing and Quality Assurance: Applications, Opportunities, Challenges, and Future Perspectives

Artificial intelligence (AI) is increasingly influencing pharmaceutical manufacturing and quality management by enabling data-driven process monitoring, prediction, automation, and decision support. This narrative review examines the application of AI across major pharmaceutical manufacturing operations and within quality assurance (QA) and quality control (QC). The review focuses on machine learning, deep learning, computer vision, natural language processing, predictive analytics, Internet of Things (IoT)-enabled monitoring, robotics, and digital-twin technologies. Potential applications include raw-material inspection, dispensing and weighing, granulation, blending, tablet compression, capsule filling, coating, packaging, warehouse management, predictive maintenance, documentation review, deviation management, corrective and preventive action (CAPA), validation support, risk assessment, visual inspection, laboratorydata interpretation, stability studies, and process analytical technology (PAT). AI can improve speed, consistency, traceability, and early detection of process or quality problems; however, these benefits depend on data quality, appropriate validation, cybersecurity, system integration, human oversight, and regulatory acceptability. Current regulatory activity, including the U.S. Food and Drug Administration's discussion paper on AI in drug manufacturing, demonstrates the growing importance of risk-based approaches to AI adoption. Overall, AI should be viewed as an enabling technology that augments, rather than replaces, pharmaceutical professionals. Responsible implementation combining validated digital systems with human expertise may support safer, more efficient, and more reliable pharmaceutical manufacturing.