Artificial intelligence is increasingly being linked with Process Analytical Technology and digital twin concepts as pharmaceutical manufacturing moves toward continuous, adaptive, and data-rich production. However, the scope, maturity, and implementation readiness of this literature remain unevenly characterized. This scoping review maps the breadth of published evidence on artificial intelligence applications in pharmaceutical manufacturing involving Process Analytical Technology and/or digital twins. It identifies key concepts, methodological patterns, manufacturing contexts, and evidence gaps relevant to future research and translation. The review was guided by the Arksey and O’Malley scoping review framework and reported in alignment with PRISMA-ScR. Electronic searches were conducted across PubMed, Scopus, IEEE Xplore, and Web of Science, followed by data charting and thematic synthesis. The literature included a large volume of proof-of-concept studies using machine learning models on Process Analytical Technology data. Digital twin applications were fewer and were concentrated mainly in continuous manufacturing, while full integration of Process Analytical Technology, artificial intelligence, and digital twins was rarely reported. The field is technically promising but academically fragmented. The principal gaps concern prospective industrial validation, regulatory alignment, interoperable data infrastructure, and the absence of integrated Process Analytical Technology–artificial intelligence–digital twin systems.