###### Abstract
Such 3 DP technologies have been actively researched and used in the construction industry in recent years because of the geometric complexity and material efficiency they can facilitate compared to traditional building methods [20]. However, there are still many challenges and opportunities for improvement regarding this technology [21; 22], such as low print accuracy or unreliable mechanical properties of printed parts. AI tools were recognized as a prospective method for overcoming these challenges [23]. Previous state-of-the-art studies have been found in the fields of 3 DP for large-scale architectural structures [22; 24] and the adoption of AI technologies in the construction industry [25; 26], yet no review study has been found to explore the application and correlation of the combined technologies in architectural design and fabrication, apart from several reviews on Machine Learning (ML) in 3 DP [27; 28]. ML, specifically, has successfully been used to optimize 3 DP processes in engineering industries, as presented in the study by [PERSON] [29], offering a comprehensive review of ML in 3 DP across several engineering disciplines. Other AM challenges have also been addressed using different AI tools. For example, real-time defect detection is achieved using deep learning tools [30; 31; 32], while several ML methods are used for porosity predictions for 3D-printed structures [33; 34]. The implementation of these tools in the construction industry is still in the early stages of research; however, several studies have recognized both 3 DP and AI as technologies with a high potential for development and increased use in the next decade [25; 35]. To the best of the authors' knowledge, no study has provided a comprehensive overview of the application of several AI techniques used for the design and production planning of large-scale 3D-printed structures in architecture up to this point. Regarding the above, the objective of this study is to identify, evaluate, and provide a summary of the relevant studies. The specific tasks include the systematization of the available knowledge, including research papers, journal articles, conference proceedings, and other relevant sources, to establish the scope of the current state of the field. The purpose of the study is to provide an in-depth understanding of the field, identify research gaps, and assess technological advancements. This knowledge can contribute to further research, inform decision making, and promote the effective integration of AI and 3 DP in architectural practice. Furthermore, the study seeks to understand how AI techniques have enhanced the design, fabrication, and performance of large-scale 3D-printed architectural structures. This purpose helps researchers and practitioners stay informed about the latest technological developments and potential applications. Finally, the study intends to explore the practical implications of AI in the 3 DP of architectural elements. The systematic literature review (SLR) method was used to identify, categorize, evaluate, and report relevant literature on the topic, extract and interpret data, and derive conclusions about the questions under consideration. Conducting the research included the following activities: forming a literature sample as the result of a database search and selection of relevant publications; analyzing the literature sample using quantitative and qualitative methods; summarizing the evidence; interpreting the findings; and discussing recognized opportunities and challenges for future research and application of these structures in the industry. This study promotes innovative design, informs ways of affecting material- and fabrication-related decisions through intelligent tools and techniques, supports sustainability goals, and provides an insightful review of the cases. By exploring the intersection of AI and AM, researchers can shape the future of architecture and construction toward creating a more sustainable environment.