Now dental caries, tooth decay or cavities, affect people of all ages worldwide. Despite advances in dental care and prevention, dental caries remains common. This requires innovative management methods. Visual examination is often insufficiently sensitive and reproducible for caries detection, delaying diagnosis and treatment. Healthcare professionals struggle with dental care due to its complex causes and unpredictable progression. Recently, machine learning (ML) algorithms have shown promise in dentistry and other medical fields. Data-centric machine learning techniques analyze intricate patterns in large datasets to enable early detection, precise diagnosis, and customized treatment planning. Machine learning models can predict risk, detect lesions, and improve treatment outcomes using clinical, radiographic, and demographic data. Dental caries management and prevention benefit from machine learning (ML). This study compares Random Forest, XGBoost, CNN, and YOLO machine learning models for predicting and diagnosing dental caries using panoramic dental datasets. The presented work examines how Ant Colony Optimization (ACO) improves Machine Learning (ML) model efficiency. This study uses panoramic dental datasets to evaluate machine learning models for dental caries management. We test Random Forest, XGBoost, CNN, and YOLO for predictive power, each with unique feature extraction, classification accuracy, and computational efficiency. We also assess how ACO optimization improves these models’ predictive accuracy and robustness. The presented research seeks to inform healthcare professionals, scientists, and decision-makers about machine learning-based precision medicine strategies for dental care and oral health.