Digital twins offer a paradigm for real-time, predictive, and adaptive control in pharmaceutical manufacturing. Their relevance has increased with the expansion of continuous manufacturing, Process Analytical Technology, and model-informed quality assurance, but their adoption remains uneven and conceptually fragmented. This scoping review maps the extent, range, and nature of research on digital twins in pharmaceutical manufacturing from 2017 to 2026. The review focuses on process modeling, PAT integration, validation strategies, regulatory positioning, and applications related to real-time release testing. A scoping review was conducted using peer-reviewed literature indexed across PubMed, Scopus, Web of Science, and IEEE Xplore. The review followed a structured evidence-mapping approach based on population, concept, and context criteria for pharmaceutical manufacturing digital twins. The evidence base is concentrated in continuous solid dosage manufacturing, biopharmaceutical processing, and selected model-based control environments. Mechanistic and hybrid models dominate the literature, while end-to-end PAT-coupled validation and prospective real-time release applications remain limited. Digital twin research in pharmaceutical manufacturing is growing but remains largely proof-of-concept. Significant work is needed to establish regulatory-grade validation, lifecycle model management, and GMP-compatible evidence standards.