In the emerging Pharma 4.0 paradigm, pharmaceutical factories adopt Industry 4.0 digitalization (AI, IoT, etc.) to become “smart” manufacturing sites, improving throughput and enabling right-first-time processes. Integration of AI with Process Analytical Technology (PAT) provides a data-rich platform for real-time process understanding, consistent with Quality by Design (QbD) principles of building quality into manufacturing. For example, spectroscopic PAT methods such as near-infrared (NIR) and Raman spectroscopy are widely employed to monitor blend uniformity, moisture, and crystallinity; AI-driven multivariate models can analyze these data to predict and control critical quality attributes on-the-fly, yielding improved consistency and reduced waste. This AI–PAT synergy has accelerated the development of complex products: for instance, COVID-19 mRNA vaccines were brought to market in months (instead of years) by leveraging machine learning and in-line PAT for rapid formulation and scale-up. Moreover, AI-enabled PAT enhances cost-efficiency and reduces labor needs in manufacturing. High automation can lessen the physical and cognitive demands on a limited workforce, and continuous PAT monitoring reduces the need for highly specialized operators, helping to address skill shortages. Process optimization with AI also allows smaller plant footprints and fewer production steps; these reductions in footprint and complexity lower capital investment and operating costs. Together, these benefits align with the Pharma 4.0 vision of efficient, QbD-driven development—enabling faster, more cost-effective drug development with built-in quality and lower resource use.