Laparoscopic surgery, despite its minimally invasive benefits, faces challenges like limited visualization and complexity. Artificial intelligence (AI), particularly machine learning and deep learning, offers revolutionary tools to enhance surgical quality. This review surveys AI applications across the laparoscopic surgical workflow, focusing on its power to improve precision, safety, efficiency/standardization, training, and personalization. AI enhances precision by optimizing surgical planning and guiding intraoperative navigation through real-time anatomical recognition. It bolsters safety via preoperative risk assessment, intraoperative hazard warnings (e.g., hemorrhage), vital structure protection, and postoperative complication prediction. AI promotes efficiency and standardization by automating planning, recognizing surgical phases for workflow optimization, and enabling objective quality assessment. For training, AI provides objective skill evaluation using video, kinematic, and physiological data, potentially accelerating learning curves with personalized feedback. Furthermore, AI facilitates personalization through risk stratification, outcome prediction, and risk-driven follow-up, advancing precision surgery.Despite the remarkable potential demonstrated by AI in laparoscopic surgery, challenges remain, including data privacy, cross-center generalization, model interpretability, and clinical integration. Future research should focus on enhancing multimodal data sharing and federated learning, the design of explainable models, and deep integration with robotic systems, to propel laparoscopic surgery towards greater intelligence, safety, and efficiency, thereby laying the technological foundation for precision surgery.