This paper focuses on the use of CNNs for diagnosis from CBCT images of dental conditions. In general, CBCT is becoming increasingly commonplace in dental imaging techniques in terms of creating high-resolution images to evaluate almost every dental condition, especially periodontal diseases, dental implants, and maxillofacial malformations. The dataset consisted of 1,000 annotated CBCT images collected from a dental clinic to reflect a mix of the aforementioned conditions. As such, the overall accuracy was 92%, and the high values of sensitivity were achieved at 89% for periodontal diseases and dental implants, 93% for dental implants, and 95% for maxillofacial abnormalities while the very high specificity values ensured the model’s reliability in proper identification of the negative cases. Other performance metrics, including precision and F1 scores, suggested that the model performed well, especially in the differentiation among the various dental conditions. On the negative side, there existed a 6% rate of misclassification between periodontal diseases and maxillofacial abnormalities, implying possible areas for improvement. The results here clearly validate the potential of CNNs in enhancing the quality of diagnostics provided utilizing dentistry and strongly suggest that their integration into AI-driven technologies in dental imaging might have a very strong impact on clinical practice and patient care.