<p>Ceramic additive manufacturing (AM) has advanced significantly since its inception in the 1980s, enabling the fabrication of complex geometries and functionally graded materials that were previously unattainable using traditional manufacturing methods. While novel modeling methods have been developed to improve the printing process in terms of efficiency and cost, a gap remains in reviewing their application and identify potential areas for incorporating recent advancements in computational modeling particularly AI-driven models within a thematic framework. This review addresses that gap by focusing on the computational modeling and optimization of the ceramic AM process, part design, and structure, outlining their advantages and limitations through a comprehensive set of research questions related to ceramic process modeling, monitoring, and optimization strategies, along with their corresponding computational modeling applications. It explores the use of artificial intelligence and machine learning (ML) techniques in process optimization, defect detection, and quality control. This in-depth review offers insights into state-of-the-art computational modeling techniques for ceramic AM, providing a foundation for researchers and practitioners to tackle existing challenges and further develop the field.</p>