<p>This study enlightens the emerging research on Gen- AI in logistics, supply chains, and manufacturing by outlining its evolution, benefits, trends and future research directions and limitations. This study employed a systematic literature review (SLR) combined with bibliometric analysis. This study also performs content analysis using bibliographic coupling to examine the selected literature. The SCOPUS database was the main source, and it used a keyword-based search method to find research studies that were relevant. The initial extraction resulted in 512 documents after adopting inclusion–exclusion criteria which led to the 53 documents to be included for further analysis and discussion. Recent studies trends in AI including explainable AI, data management, robustness, real-time optimization, human-AI collaboration, and scalable deployment point to important areas for future research. In order to make AI work in the real world, AI researchers, supply chain specialists, and industry experts need to work closely together to make better decisions and solve these problems. The results give industry professionals a strategy plan for how to use Generative AI to make supply chains more efficient, open, and better at making decisions. They stress the need for AI systems that can grow and focus on people, and they help businesses and governments work together to make sure that technology is used effectively in manufacturing and logistics. This study is unique in that it explores the multiple roles of Gen-AI and provides a comprehensive picture of the research domain.</p>