Abstract Artificial Intelligence (AI) and Machine Learning (ML) are foundations in new manufacturing paradigms, yet their application in the aircraft industry remains limited, as this industry’s expertise does not traditionally cover these technologies. Additionally, due to its specific features, the aircraft industry presents unique challenges, for instance, with data scarcity. To date, no systematic review has considered these features to enable stakeholders in this sector to undergo AI/ML transformation successfully. This study aims to analyze the state of the art by providing a PRISMA systematic literature review of 135 articles, focusing on the contexts, models, and methods employed in the development of AI/ML solutions. The authors propose a framework to summarize the findings on the development, applications, benefits, and challenges of AI/ML in the aircraft manufacturing industry. In addition, further research opportunities are identified through a comparison of current research applications, theoretical concepts of Industry 5.0, and cutting-edge technologies, such as Federated Learning, Transfer Learning, the use of Large Language Models (LLMs), the lack of supply chain investigation, and the integration of human factors, which are absent in major reviewed articles. This study contributes to the field by meticulously gathering methodologies and approaches that address and integrate the specificities of AI/ML use and integration in this high-value-added industry. It bridges the gap between cutting-edge research and practical industry needs, delivering actionable insights to drive innovation and guide strategic decision-making.
BOUGAULT et al. (Sun,) studied this question.