This retrospective cohort study aimed to develop a novel prediction model for secondary bone augmentation (SBA) needs following guided bone regeneration (GBR) procedures in dental implant sites. The study utilized Python-based computer vision algorithms for cone-beam computed tomography (CBCT) analysis and compared their efficacy to conventional CBCT measurement methods. The study included 37 patients (76 implant sites) who underwent GBR procedures. CBCT scans were taken before GBR (T1) and prior to implant placement (T2). Clinical and demographic data were collected, including systemic health factors. CBCT analysis was performed using both conventional ridge width measurements and a novel Python-based computer vision approach that analyzed slice volumes. Risk indicators for SBA were identified using innovative variable selection techniques. The diagnostic efficacy of both CBCT analysis methods in predicting SBA needs was evaluated using Receiver Operating Characteristic (ROC) curves and Area Under the Curve (AUC) values. Results showed that the Python computer vision analysis (AUC = 0.876) demonstrated slightly superior predictive capability compared to conventional measurements (AUC = 0.847). Several clinical factors were identified as significant predictors of SBA needs, including diabetes mellitus, smoking history, and specific bone graft materials. This study demonstrates the potential of advanced computational approaches in improving the prediction of SBA needs following GBR procedures. The integration of detailed CBCT analysis with clinical factors provides a promising framework for personalized treatment planning in dental implantology. Future research with larger sample sizes and standardized GBR procedures is recommended to further validate and refine this predictive model.