OBJECTIVES: To assess Artificial Intelligence (AI) platforms, machine learning methodologies and associated accuracies used in detecting dental caries from clinical images and dental radiographs. METHODS: A systematic search of 8 distinct electronic databases: Scopus, Web of Science, MEDLINE, Educational Resources Information Centre, Institute of Electrical and Electronics Engineers Explore, Science Direct, Directory of Open Access Journals and JSTOR, was conducted from January 2000 to March 2024. AI platforms, machine learning methodologies and associated accuracies of studies using AI for dental caries detection were extracted along with essential study characteristics. The quality of included studies was assessed using QUADAS-2 and the CLAIM checklist. Meta-analysis was performed to obtain a quantitative estimate of AI accuracy. RESULTS: Of the 2538 studies identified, 45 met the inclusion criteria and underwent qualitative synthesis. Of the 45 included studies, 33 used dental radiographs, and 12 used clinical images as datasets. A total of 21 different AI platforms were reported. The accuracy ranged from 41.5% to 98.6% across reported AI platforms. A quantitative meta-analysis across 7 studies reported a mean sensitivity of 76% [95% CI (65% - 85%)] and specificity of 91% [(95% CI (86% - 95%)]. The area under the curve (AUC) was 92% [95% CI (89% - 94%)], with high heterogeneity across included studies. CONCLUSION: Significant variability exists in AI performance for detecting dental caries across different AI platforms. Meta-analysis demonstrates that AI has superior sensitivity and equal specificity of detecting dental caries from clinical images as compared to bitewing radiography. Although AI is promising for dental caries detection, further refinement is necessary to achieve consistent and reliable performance across varying imaging modalities.