Background: The study aimed to comprehensively assess the precision and performance of the AI-based dental disease detection technology DentalFriend within a dental camp population. Dental camps offer an ideal environment to evaluate Dental Friends effectiveness, particularly in resource-limited settings. The study aimed to enhance oral health assessment in dental camps, focusing on a sample size of 5000 participants spanning the age group of 5 to 74 years. Materials and Methods: Employing a cross-sectional design, the study enrolled 5000 participants attending dental camps. DentalFriend conducted oral examinations, capturing images for AI analysis, while clinical examinations by dental professionals served as the gold standard. Diagnostic accuracy metrics, subgroup analyses based on the age group of 5-74 years, statistical comparisons, and agreement analysis using Cohen's Kappa coefficient were performed. Results:DentalFriend demonstrated remarkable diagnostic accuracy with consistent sensitivity, specificity, PPV, NPV, and overall accuracy values all at 98.4%. Subgroup analysis within the age group of 5-74 years revealed DentalFriends stable performance. Statistical analysis indicated significant differences (p-value = 0.004) between DentalFriend and clinical examination outcomes. Agreement analysis displayed substantial concordance (Kappa = 0.92). Conclusion: The study established DentalFriend as a reliable diagnostic tool in dental camps for a wide age range. With high accuracy across demographic segments, it offers potential utility in diverse oral health assessment settings. The significant statistical differences and substantial agreement with clinical examination outcomes underscore its clinical viability. These findings contribute to enhancing oral health evaluation in dental camps and broader healthcare contexts.