Liver cancer, as a globally prevalent malignant tumor, poses significant challenges to minimally invasive surgical treatment due to its complex anatomical structure and clinical characteristics. The rise of artificial intelligence (AI) technology offers a revolutionary solution to this predicament. This article systematically reviews the current application status of AI in the entire process of minimally invasive surgery for liver cancer, including preoperative planning, intraoperative navigation, and postoperative management. In the preoperative stage, AI integrates multimodal data to achieve three-dimensional liver reconstruction, surgical risk assessment, and prediction of tumor biological behavior (such as microvascular invasion), providing a quantitative basis for the formulation of individualized surgical plans. During the intraoperative phase, AI technologies such as augmented reality navigation, real-time anatomical structure recognition, and risk warning significantly enhance surgical precision and safety, while optimizing the surgical process to promote standardization. In postoperative management, AI models integrate multi-dimensional data to build high-precision prognosis prediction and complication risk assessment systems, combined with intelligent follow-up platforms, to achieve stratification of recurrence risk and full-course individualized management. Despite the significant potential of AI technology, issues such as insufficient data quality and standardization, limited model generalization ability, clinical integration obstacles, lack of decision interpretability, and data privacy and security concerns still restrict its large-scale clinical transformation. In the future, with the development of multimodal data fusion, federated learning, and explainable AI, AI is expected to drive minimally invasive liver surgery towards standardization, intelligence, and universalization, ultimately achieving the goal of precision medicine.