Artificial intelligence (AI) has significantly impacted dentistry by enhancing diagnostic accuracy, treatment planning, and patient care across various specialties, including endodontics, radiology, and periodontology. This review synthesizes findings from five key studies examining AI applications in dentistry, focusing on convolutional neural networks (CNNs) and deep learning models.AI-assisted diagnostics have shown superior accuracy compared to traditional methods, with CNNs achieving up to 94% accuracy in detecting periapical lesions and surpassing human radiologists in specific diagnostic tasks. Additionally, AI-assisted caries detection improves tooth retention and reduces treatment costs, demonstrating its potential economic benefits. However, challenges such as data biases, ethical considerations, and regulatory barriers remain future research should focus on developing transparent AI models, standardizing datasets, and addressing cost-effectiveness concerns to enhance clinical integration. Methods: A comprehensive review of five peer-reviewed articles was conducted, highlighting AI applications in dentistry. The articles were selected based on relevance to diagnostic advancements, clinical decision-making, and patient outcomes. Key methodologies included CNN-based image analysis, deep learning applications for caries detection, and neural networks for treatment optimization. Results: AI applications in dentistry demonstrated superior diagnostic performance. CNNs achieved 94% accuracy in detecting periapical lesions and surpassed human radiologists in specific diagnostic tasks. AI-assisted caries detection improved tooth retention by 62.8 years on average, with cost savings of €378 per patient compared to traditional methods. In endodontics, AI accurately identified root fractures and predicted treatment outcomes with up to 95.6% accuracy. Despite these advancements, limitations such as data biases and interpretability of AI models were noted. Conclusion: AI holds transformative potential for modern dentistry by improving diagnostic precision and clinical efficiency. However, integrating AI into routine practice requires addressing data standardization, ethical frameworks, and regulatory barriers. Future research should focus on developing transparent AI models and exploring their cost-effectiveness and long-term impact on patient care.