Artificial intelligence (AI) has shown great promise in improving dental diagnoses and treatment. This paper investigates the utilization of AI, specifically deep learning and machine learning in dental diagnostics, particularly for the identification of dental issues that are often observed in substance abusers such as periodontal disorders, dental caries, xerostomia, and gingivitis. A comprehensive literature search was performed utilizing the Medline (PubMed) and Google Scholar databases to identify suitable papers, resulting in the selection of 12 studies. While we did not find any studies specifically focused on substance abusers, we examined existing research on AI for common dental problems. The chosen studies used various imaging modalities, including intraoral pictures, periapical radiographs, and panoramic radiographs relevant to substance abusers. Convolutional Neural Networks, such as ResNet, VGG, GoogLeNet, and Inception, are frequently employed as architectural models for image classification. Object Detection often relies on popular architectures such as YOLO or Faster R-CNN. Image Segmentation utilizes neural network topologies such as U-Net or Mask R-CNN. This analysis serves as a basis for future research that would specifically address substance abusers. Future endeavors should also prioritize the expansion of datasets to enhance the model’s ability to apply it to a wide range of situations and integrate the concept of explainability into decision-making procedures. In general, AI can revolutionize dentistry diagnostics by facilitating prompt and precise diagnosis, leading to enhanced patient outcomes.