Background <p>Automation and artificial intelligence (AI) are increasingly applied to digital dentistry, aiming to improve efficiency and accuracy in restorative and surgical workflows. The precise design of mandibular first molar occlusal surfaces remain a time-consuming manual task that limits scalability. This study aimed to develop and validate a fully automated AI framework for individualized occlusal surface reconstruction from intraoral scans, supporting the integration of automation into prosthodontic procedures.</p> Methods <p>Intraoral scans from 110 patients were partitioned into three cohorts: a training set (<i>n</i> = 90), a validation set (<i>n</i> = 10), and a testing set (<i>n</i> = 10). Occlusal surfaces of maxillary premolars and molars were encoded into a 30-dimensional latent space via a Dimension Elevator Module. An attention-guided fusion network integrated multi-scale geometric features to reconstruct mandibular first molar occlusal surfaces. Performance was assessed using point-to-point error, Dice similarity coefficient (DSC), and surface normal cosine similarity. Clinical validation was conducted through structured evaluation by ten dental professionals using a five-point Likert scale.</p> Results <p>The framework achieved a mean point-to-point error of 0.66 mm, a DSC of 86.9%, and a surface normal cosine similarity exceeding 87%. Expert evaluation yielded mean scores above 4.0 across all criteria, with favorable ratings in occlusal accuracy (4.08 ± 0.78), clinical feasibility (4.09 ± 0.78), and overall acceptability (4.10 ± 0.78).</p> Conclusions <p>The proposed AI-driven workflow enables automated and clinically validated occlusal surface design, demonstrating both quantitative accuracy and expert endorsement. By streamlining digital prosthodontic workflows, this approach aligns with the broader trend of robotics and automation in dental surgery, offering potential to enhance surgical precision, reduce manual workload, and improve patient outcomes.</p>