Machine learning–driven innovation in transdermal drug development

Transdermal drug development is substantially constrained by three major challenges, including the formidable skin barrier, complex nonlinear interactions among formulation components, and instability in manufacturing processes. Traditional empirical and mechanistic models often demonstrate limited generalizability across different protocols and chemical spaces. Objective This review aims to systematically map the applications of machine learning (ML) throughout the transdermal drug development pipeline, identify key challenges related to data quality, validation, interpretability, and regulatory translation, and propose a regulatory-aligned framework for integrating ML into quality-by-design (QbD) and process analytical technology (PAT) strategies. Methods A systematic literature review covering studies published between 1996 and 2026 was conducted, with critical synthesis of ML models, evidence-based applications, and emerging future directions. Results Tree-based ensemble models, including random forest (RF) and XGBoost, appear particularly suitable for small and heterogeneous transdermal datasets. Hybrid physics-informed ML approaches improve extrapolation capability, while deep learning demonstrates strong performance in image-based evaluation tasks. Major challenges include the lack of standardized datasets, variability in experimental protocols, insufficient external validation, and limited interpretability associated with “black-box” models. This review further proposes a problem–algorithm–solution framework, a minimum metadata schema for data harmonization, and a regulatory-oriented roadmap to support industrial translation. Conclusion ML has the potential to enable data-driven, end-to-end digitalization of transdermal development. This review provides a unified, practice-oriented, and regulatorily compliant guide to facilitate the reliable, interpretable, and scalable implementation of ML in transdermal drug delivery research and development.