Artificial Intelligence In Pharmaceutical Process Validation: A Review

Taufik Mulla*, Siddheshwar Sonavane, Ayush Tambe, Megha Hange, P. N. Sable

For decades, pharmaceutical process validation has rested on a relatively narrow set of habits: a fixed qualification protocol, a small handful of conformance batches, and a periodic review of trends to argue that manufacturing is operating consistently. That posture is being challenged. Over roughly the last ten years, manufacturers and regulators have begun exploring how techniques drawn from artificial intelligence — including machine learning, deep learning, artificial neural networks, fuzzy logic, and digital twin modelling — can be brought into every phase of the validation lifecycle. This narrative review gathers the most informative recent literature on the topic and weaves it into a single account aimed at practitioners, quality leaders, and academics alike. United States Food and Drug Administration's three-stage framework — process design, process qualification, and continued process verification — and traces how artificial intelligence is being combined with Process Analytical Technology and Quality by Design to enable real-time monitoring, predictive quality control, and adaptive process understanding. It then turns to the regulatory environment, where instruments such as the International Council for Harmonization Q8–Q13 series, GAMP 5 Second Edition, the FDA's Artificial Intelligence and Machine Learning Action Plan, and the European Union Artificial Intelligence Act are beginning to define how adaptive, data-driven models can be qualified and maintained under Good Manufacturing Practice. Persistent obstacles — model drift, fragmented data, limited explain ability, cyber risk, and the absence of harmonized international guidance — are highlighted as genuine barriers rather than solved problems.