The application of deep learning techniques in dentistry has revolutionized the diagnostic and treatment approaches. This study aims to evaluate the impact and trends of deep learning methodologies in dentistry through a bibliometric analysis. A comprehensive search in the Web of Science database yielded 1228 articles published between 2014 and 2024. Bibliometric analyses, including keyword co-occurrence, co-authorship, citation patterns, and collaborative networks, were conducted using VOSviewer software. Remarkably, 94.95% of these publications were released after 2018, showcasing a significant surge in research interest in recent years. The USA and Japan emerged as leading contributors, accounting for 20.6% and 18.48% of the articles, respectively. Notable institutions, such as Tokyo Medical and Dental University, stood out with 88 publications. Key contributors like Orhan Kaan, Schwendicke Falk, Ariji Yoshiko, and Ariji Eiichiro made substantial impacts. The analysis revealed clusters of interconnected research areas, collaborative networks among authors and countries, and influential publications. This study offers valuable insights into the evolving role of deep learning in dentistry, underscoring the growing global interest and collaboration in the field. Understanding these trends is essential for shaping future research directions and fostering interdisciplinary partnerships. The integration of deep learning in dentistry represents a paradigm shift in diagnostic accuracy, treatment effectiveness, and patient-centered care, driving interdisciplinary and international collaborations that promise transformative advancements in dental practice.