Dental implantation restores missing teeth through surgical insertion of artificial roots, relying on preoperative digital planning to ensure precision and efficiency. However, critical challenges persist in virtual tooth positioning: this process demands extensive clinical expertise and time-consuming manual adjustments due to ambiguous anatomical references from missing teeth. To address these limitations, we propose a unified framework that accurately predicts the original three dimensional (3D) shapes and positions of missing teeth in diverse patterns, enabling anatomy-aware preoperative planning. Our proposal introduces two technical innovations: (1) A dynamic iterative generation strategy is proposed to progressively predict multiple missing teeth one by one using a target tooth identification module, accommodating arbitrary tooth loss patterns without case-specific retraining; (2) A tooth-centroid-prompted conditional diffusion model is developed to leverage geometric constraints from predicted tooth centroid and adjacent teeth to generate high-fidelity point cloud reconstructions. Extensive experiments show that our model outperforms conventional U-net based framework in predicting multiple missing teeth, achieving a prediction accuracy of 1.30 mm (Chamfer Distance) and an angular error of 5.42 \(^\circ \) . This improvement has the potential to enhance the accuracy and efficiency of dental implant planning by providing precise anatomical references for missing teeth, potentially revolutionizing digital dentistry workflows.