Integration of quality by design with artificial intelligence/machine learning technologies in pharmaceutical manufacturing: A comprehensive review

Background and purpose: Quality by design (QbD) has revolutionized pharmaceutical development by shifting from a conventional quality-by-testing approach to a science-based approach that incorporates quality into products and processes from inception. The integration of artificial intelligence (AI), machine learning (ML), automation, and digital technologies with QbD offers new opportunities to enhance process understanding, optimize critical quality attributes (CQAs) and support the Pharma 4.0 industry framework. Experimental approach: A systematic review of studies published between 2016 and 2025 was conducted to evaluate the application of supervised learning algorithms, deep neural networks, reinforcement learning, and digital twin technologies within QbD. Regulatory frameworks, including International Council for Harmonisation (ICH) quality guideline Q8 through Q14, the U.S. Food and Drug Administration’s Emerging Technology Program and the European Medicines Agency quality initiative, together with representative industrial case studies, were also examined. Key results: This review demonstrates that AI-driven QbD improves process understanding, CQA optimization, batch-to-batch consistency, process robustness, and real-time quality control. Further, advanced computational models enable predictive process monitoring, intelligent decision-making, and in silico experimentation, reducing manufacturing costs, development time and experimental burden. However, challenges related to data quality, model interpretability, validation, and regulatory harmonisation remain a critical barrier to widespread implementation. Conclusion: The convergence of AI, ML and QbD provides a robust framework for intelligent pharmaceutical manufacturing (PM). AI-enabled QbD supports the development of high-quality products through predictive, data-driven, and in silico approaches while accelerating development and improving manufacturing efficiency. These technologies offer a practical roadmap toward Pharma 4.0 and the future of digital PM.