Global Trends in Integrating Machine Learning (ML) with Model-Informed Drug Development (MIDD): A Bibliometric and...

Background/Objectives: The integration of machine learning (ML) within model-informed drug development (MIDD) represents a rapidly evolving paradigm in pharmacometrics, enabling improved prediction, optimization, and regulatory decision-making across drug development pipelines. However, the extent to which ML methods are explicitly integrated into regulatory decision-making remains limited and unevenly characterized. This study aims to systematically map the ML-MIDD scholarly landscape, identify core sources and contributors, assess thematic evolution, and summarize key methodological advancements through an integrative bibliometric and systematic review. Methods: A comprehensive literature search was conducted across Web of Science, Scopus, and PubMed (2015-2025), followed by metadata harmonization and deduplication. Bibliometric analysis was performed using Bibliometrix, VOSviewer, and PRISMA guidelines to characterize publication trends, collaboration patterns, thematic structures, and representative methodological contributions. Results: A total of 770 records were initially retrieved, with Scopus contributing the largest share (n = 343; 44.5%), followed by Web of Science (n = 322; 41.8%) and PubMed (n = 105; 13.6%). After deduplication, 607 unique publications remained (78.8% of total), and 560 were included in the final systematic review (97.6% of full texts). Publications spanned 269 sources, with core journals accounting for 28% of output. The United States led in volume (n = 665; 20.8%) and international collaboration (16.47%). Thematic evolution revealed transitions from foundational PK/PD methods (2016-2018) to applied ML-driven precision pharmacology (2022-2025). Conclusions: Emerging methods included deep learning, reinforcement learning, and hybrid mechanistic-ML models. ML-MIDD is a rapidly maturing interdisciplinary field, evidenced by expanding methodological diversity and increasing use of ML-enabled components within regulatory-relevant modeling workflows, rather than formal regulatory endorsement of ML-MIDD as a standalone methodology, indicating growing translational relevance but continued need for validation and regulatory clarity.