<![CDATA[The agricultural product supply chain frequently faces challenges, including fluctuations in demand, climate change, and the perishable nature of products, which can result in inefficiencies and losses. These issues require technology to optimize supply chain performance, one of which is through the use of Artificial Intelligence (AI). This study aims to identify the types of AI commonly used, their applications across various stages of the supply chain, their role in enhancing efficiency, and the challenges associated with their implementation. The method used is a Systematic Literature Review (SLR) based on 21 scientific articles from 2015 to 2025 sourced from the Scopus database. Articles were selected based on criteria including journals and proceedings, open access, and relevance to AI applications in agricultural product supply chains. The research results indicate that machine learning and deep learning are the most widely used types of AI, particularly for crop yield prediction, plant disease detection, product quality classification, and logistics management. AI has been applied across various stages of the supply chain, from cultivation, processing, to distribution. AI has proven to enhance efficiency, real-time monitoring, and decision-making. However, its implementation still faces challenges such as limited quality data, inadequate infrastructure, high implementation costs, and low human resource capacity. Therefore, the utilization of AI in the agricultural product supply chain requires collaboration between the government, academia, industry, and farmers. On the other hand, regulations and policies supporting AI adoption also need further review to ensure this technology can be widely and sustainably implemented.]]>
