We employed a three-phase approach, culminating in a randomized controlled trial, to assess the efficacy of 3D-printed liver models in hepatobiliary surgical planning. Phase one involved developing and selecting 35 optimal 3DP models based on timeliness, cost, precision, and alignment with digital simulations. Phase two utilized deep learning algorithms to optimize the 3D reconstruction process, significantly enhancing efficiency and accuracy compared to manual segmentation. In phase three, a randomized controlled trial with 64 patients compared surgical outcomes between those planned with AI-enhanced physical 3DP models and those with traditional digital simulations. Results demonstrated that 3DP models were produced rapidly (3.52 h at $152 each) with high precision, AI-assisted reconstruction reduced processing time (303.5 vs. 557 min), and patients using AI-enhanced physical 3DP models experienced less intraoperative blood loss. Integrating deep learning with 3D printing offers a cost-effective, scalable method to enhance surgical planning and outcomes in hepatobiliary surgery.