<![CDATA[The use of artificial intelligence (AI) in the procurement sector, especially to select and evaluate suppliers, is currently developing along with the increasingly complex supply chain network and accessibility to large amounts of data. Supplier selection and evaluation methods that are commonly used are conventional methods such as multi-criteria decision-making (MCDM) methods and fuzzy-based approaches, which rely heavily on human assessment and are less adaptive to changes in supply chain environmental conditions. The authors conducted a systematic review following the PRISMA guidelines to evaluate the development of AI utilization in supplier selection and evaluation methods. A total of 21 articles published between 2015 and 2025 in the Scopus database and meeting the set inclusion criteria were used for analysis. The results show the development of AI methodologies, ranging from soft computing approaches to hybrid models and machine learning methods. AI roles in decision-making has also transitioned from being a data processing tool to acting as an automated decision maker using predictive models. However, this study also identifies several challenges, such as dominance of static models, limited use of unstructured data and ESG metrics, and practical implementation in real world situation. This research presents a comprehensive categorization of AI methodologies and roles in decision-making framework, aiming to improve the construction of more transparent and robust AI-driven procurement systems. The findings contribute to theory and managerial practices by explaining how AI can be used to improve and automate decision making process, to support more data-driven procurement strategies.]]>
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