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Pharmaceutical 3D printing has emerged as a technically powerful approach for producing personalised, flexible, and on-demand dosage forms. Its appeal lies in the ability to vary dose, geometry, release profile, and patient acceptability without requiring a new conventional manufacturing line for every product variant. Despite this promise, the field remains constrained by a persistent mismatch between design capability and translational readiness. Many studies demonstrate sophisticated printed tablets, films, lattices, and personalised dosage forms, yet far fewer address the manufacturing controls, release strategies, and regulatory evidence needed for routine clinical implementation. This critical review evaluates pharmaceutical 3D printing through the connected lenses of design logic, manufacturing constraints, and regulatory readiness. The review treats additive manufacturing not as one technology but as a family of processes whose material requirements, process risks, and quality attributes differ substantially. The review concludes that pharmaceutical 3D printing will not translate through formulation novelty alone. A technology-agnostic, risk-proportionate regulatory pathway combined with scalable, PAT-integrated manufacturing platforms is essential to move from promise to practice.

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.