Machine Learning in Smart Hydrogel 4D Printing: Advances, Challenges, and Translational Perspectives

The convergence of artificial intelligence (AI) and smart hydrogel-based additive manufacturing offers new capabilities for fabricating adaptive structures, though the field remains in early stages of translation. Smart hydrogels, with their stimuli-responsive properties and biocompatibility, present opportunities in tissue engineering, drug delivery, and soft robotics. However, the complexity of their material behavior and the challenges of high-resolution four-dimensional (4D) printing demand advanced computational approaches to optimize design, processing, and performance. This review systematically examines the role of AI, encompassing machine learning (ML), deep learning (DL), and generative models, in addressing these challenges. We critically assess AI-driven strategies for hydrogel formulation, real-time process control, structural optimization, and biological outcome prediction, distinguishing between retrospectively fitted models and prospectively validated implementations. A key contribution of this review is the clear differentiation between AI-assisted 3D printing and genuine 4D printing, where constructs exhibit time-dependent shape change, actuation, or degradation in response to stimuli. We introduce AI applications in specific hydrogel printing domains, provide a comparative framework for distinguishing 3D from 4D systems, and critically evaluate quantitative claims with attention to dataset size, validation approaches, and reproducibility. Case studies highlight where AI enhances printability, resolution, and functional outcomes, while we also identify persistent limitations including data scarcity, model interpretability, and regulatory hurdles. A comparative analysis of AI-assisted versus traditional design-of-experiment strategies reveals that while AI offers advantages in high-dimensional spaces, traditional methods remain more accessible, interpretable, and reproducible for simpler systems. By synthesizing recent advancements with transparent methodology and practical guidance, this study provides a roadmap for future research at the intersection of AI and smart hydrogel additive manufacturing, advocating for interdisciplinary collaboration and standardized reporting to enable next-generation innovations.