Emerging Machine Learning Strategies for Digital Processing and Additive Manufacturing of Advanced Ceramics

Machine learning (ML) techniques are increasingly being applied to the development and processing of advanced ceramics, enabling predictive design, formulation optimization, and improved control of manufacturing workflows. This review presents an integrated and application‐oriented analysis of ML approaches in ceramic engineering, with particular emphasis on microstructure–property relationships, rheological modeling of ceramic suspensions, and additive manufacturing via direct ink writing (DIW). We examine how different ML methods, including neural networks, statistical regression models, active learning strategies, and physics‐informed approaches, have been used to predict phase stability, mechanical and dielectric properties, rheological behavior, and overall functional performance. In addition, we discuss recent advances in ML‐assisted microstructural characterization based on computer vision, real‐time process monitoring, and the optimization of DIW printing parameters and thermal post‐processing conditions. Key limitations are also addressed, including the scarcity of standardized datasets, experimental variability, and the challenges associated with model generalization across different material systems and processing conditions. Finally, we discuss the potential of integrating ML with automated experimentation and adaptive manufacturing systems to accelerate the development and optimization of advanced ceramics, with particular relevance for emerging research environments.