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Purpose: This study aimed to generate virtual mandibular first molars using deep learning technology, specifically deep convolutional generative adversarial network (DCGAN), and evaluate the accuracy and reliability of these virtual teeth by analyzing their morphological characteristics. These morphological characteristics were classified based on various evaluation criteria, facilitating the assessment of deep learning-based dental prosthesis production’s practical applicability. Methods: Based on our previous research, 1,000 virtual mandibular first molars were generated, and based on morphological criteria, categorized as matching, non-matching, and partially matching. The generated first molars or the categorization of the generated molars were analyzed through the expert judgment of dental technicians. Results: Among the 1,000 generated virtual teeth, 143 (14.3%) met all five evaluation criteria, whereas 76 (7.6%) were judged as completely non-matching. The most frequent issue, with 781 (78.1%) instances, including some overlapping instances, was related to occlusal buccal cusp discrepancies. Conclusion: The study reveals the potential of DCGAN-generated virtual teeth as substitutes for real teeth; however, additional research and improvements in data quality are necessary to enhance accuracy. Continued data collection and refinement of generation methods can maximize the practicality and utility of deep learning-based dental prosthesis production.