Digital-twin terminology is increasingly applied to computational models used in drug discovery, pharmacology, formulation development, drug delivery, precision dosing, clinical trials, and pharmaceutical manufacturing. However, patient-specific simulation, mechanistic modeling, adaptive prediction, virtual populations, and process monitoring are frequently described as digital twins without consistent requirements for real-entity linkage, updating, uncertainty assessment, or decision use. This umbrella review critically evaluates review-level evidence across pharmaceutical domains to determine when a model warrants the digital-twin designation. The synthesis distinguishes digital models, adaptive models, virtual patients, digital shadows, and decision-coupled twins according to counterpart identity, data connection, update logic, predictive purpose, bidirectional information flow, validation, and governance. Review selection, methodological quality, primary-study overlap, consistency, and context of use are treated as determinants of evidentiary strength rather than assuming that multiple reviews constitute independent confirmation. The evidence indicates that pharmacometric, systems-pharmacology, formulation, delivery, and manufacturing models provide important twin-enabling capabilities, but most reported applications demonstrate only subsets of the functions implied by a mature digital twin. Patient specificity, mechanistic sophistication, or benchmark performance alone does not establish twin status. The central contribution is a proposed operational boundary in which a pharmaceutical digital twin must represent a specified counterpart, receive state-relevant updates, generate uncertainty-aware prospective predictions, and connect those predictions to an auditable and bounded decision pathway. This framework is conceptual and requires domain-specific validation. Its purpose is not to promote universal adoption of digital twins, but to improve terminology, evidence appraisal, comparative evaluation, and translational judgment across computational pharmaceutical science.