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Continuous manufacturing has been promoted as a transformative alternative to batch pharmaceutical production because it can reduce equipment footprint, shorten development-to-commercialisation timelines, and enable more responsive quality assurance. Its appeal rests on the idea that material flows through an integrated process rather than waiting in isolated unit operations. Yet the technical maturity required to make that flow reliable is often understated. The central problem is that continuous operation is sometimes treated as intrinsically superior to batch production, as though continuity alone guarantees better quality. In practice, quality depends on the ability of sensors, models, actuators, supervisory logic, and operators to detect and correct deviations fast enough to prevent poor material from propagating through the line. Without that control capability, continuous systems may amplify rather than resolve process vulnerability. This critical review examines continuous pharmaceutical manufacturing through three linked lenses: control architecture design, process robustness, and regulatory translation. It asks whether current systems are sufficiently controlled to justify claims of superior quality, whether robustness is assessed with appropriate metrics, and whether regulatory frameworks have matured enough to support dynamic manufacturing strategies. The review deliberately treats continuous manufacturing as a socio-technical system rather than as a purely technological upgrade. The evidence indicates that continuous manufacturing has advanced substantially, particularly in direct compression, wet granulation, process analytical technology, residence-time modelling, soft sensing, and model predictive control. However, many demonstrations remain limited by narrow disturbance scenarios, incomplete treatment of start-up and shutdown, uncertain model maintenance requirements, and uneven translation into routine good manufacturing practice. Regulatory acceptance is progressing, but unresolved questions remain around batch definition, traceability, adaptive control validation, and lifecycle change management. The review concludes that continuous manufacturing is not an automatic guarantor of pharmaceutical quality. Its value depends on control-centric process design, standardised robustness assessment, credible digital and analytical infrastructure, and regulatory alignment that recognises dynamic process operation. The future pathway requires stronger pre-competitive collaboration, more realistic stress testing, and regulatory science that keeps pace with advanced control strategies.

Continuous pharmaceutical manufacturing has matured from a technical alternative to batch production into a strategically important manufacturing paradigm. Its promise lies in integrated material flow, smaller production footprints, improved process understanding, flexible scale-out, and the possibility of real-time quality assurance. Yet the quality systems used to govern continuous manufacturing often remain conceptually anchored in batch-era assumptions. The central problem addressed in this article is the mismatch between continuous manufacturing’s dynamic data environment and the inherited quality logic of discrete batch testing, fixed specifications, and retrospective release decisions. In batch quality logic, quality is often treated as a property confirmed after production. In continuous manufacturing, however, quality must be interpreted, predicted, and controlled while the process is still unfolding. This article proposes Lifecycle Intelligence as a theory-driven model for rethinking quality in continuous pharmaceutical manufacturing systems. Lifecycle Intelligence is defined as a quality paradigm in which process data, predictive models, control strategies, and lifecycle learning mechanisms are integrated to maintain quality dynamically across development, validation, commercial production, and post-approval improvement. The model shifts attention from isolated product release to continuous quality cognition. The argument advanced is that continuous manufacturing cannot reach its full quality and regulatory potential if it is governed primarily by batch quality logic. A lifecycle intelligence paradigm is needed to convert continuous data streams into validated quality decisions. This shift will require not only technical advances, but also new validation practices, model governance structures, regulatory dialogue, and workforce capabilities.