Transdermal drug delivery systems (TDDS) provide an alternative to traditional oral and injectable routes by delivering drugs through the skin, thereby avoiding the gastrointestinal tract and first-pass liver metabolism, enhancing bioavailability, and reducing systemic adverse effects. Recent developments in artificial intelligence (AI) have created new opportunities to address existing challenges and accelerate the development of transdermal drug delivery systems (TDDS). This review emphasizes the growing importance of AI in the design, optimization, and assessment of TDDS. Advanced machine learning and deep learning techniques, such as artificial neural networks, quantitative structure–activity relationship (QSAR) models, ensemble learning, and physiologically based pharmacokinetic modelling, show considerable promise in predicting skin permeability, drug diffusion, formulation efficiency, and pharmacokinetic responses. Furthermore, AI-based approaches facilitate the rapid screening of drug candidates and the optimization of polymer matrices, permeation enhancers, and adhesive systems, thereby reducing dependence on conventional trial-and-error experimentation.