Purpose Laparoscopic cholecystectomy(LC)is a standard procedure for managing symptomatic gallstone disease, yet it carries risks such as bile duct injury (BDI). A significant contributor to these complications is the misinterpretation of intraoperative cholangiograms (IOCs). This review examines the role of artificial intelligence (AI) in LC, its current application, efficacy, research limitations, and future directions. We also initiate an innovative project to explore the transformative potential of AI in enhancing surgical decision-making and improving procedural safety. Method A comprehensive review of peer-reviewed articles, guidelines, and expert opinions was conducted using PubMed, Medline, and Cochrane databases. Results Current AI models have demonstrated significant efficacy in identifying critical anatomical structures, achieving over 90% accuracy in recognizing the critical view of safety and unsafe zones. However, prospective data on AI's predictive capabilities for surgical difficulty in obesity/anatomical variants/acute cholecystitis, complications, and outcomes remain limited. Studies on the use of AI in intraoperative imaging interpretations were limited. Conclusion AI that enhances intraoperative imaging by accurately detecting abnormalities in the biliary tree and providing real-time feedback can potentially reduce the risk of BDI. Integration with technologies like indocyanine green fluorescence further highlights its potential. An innovative project has been initialized at Campbelltown Hospital utilizing AI’s potential to provide real-time interpretation of IOCs, revolutionising LC by reducing cholangiogram errors, detecting BDIs, and improving intraoperative imaging.