Artificial Intelligence (AI) applications are vital for optimising supply chain operations, enhancing efficiency in logistics, inventory management, and demand forecasting. AI’s real-time data analysis capabilities empower decision-making and risk management. Despite significant growth in AI supply chain research over the past two decades, a notable gap exists in comprehensive reviews that synthesise key themes. To address this, this study combines bibliometric analysis and structural topic modelling, examining a dataset of 773 articles from Elsevier’s Scopus database spanning 2002 to 2022. This study focuses on AI applications in supply chains, emphasising theme identification, publication trends, and prominent journals. Notably, the "International Journal of Production Research" emerges as the top source, with 46 articles, constituting 6.0% of the total articles. The research uncovers four thematic clusters: AI-driven cost optimisation, AI-powered decision-making, mitigating supply chain risks, and AI integration in technological advancements. Future studies in artificial intelligence applied to supply chain management should focus on employing sophisticated algorithms. At the same time, it is essential to consider issues related to privacy and security. Additionally, researchers should tackle environmental obstacles and ensure compliance with regulatory standards. These findings enhance the understanding of AI applications in supply chains, providing insight into key themes and essential keywords. These findings are highly pertinent to various audiences, including researchers, financial institutions, and industry professionals.