<![CDATA[Advances in digital technologies, particularly artificial intelligence (AI), are transforming healthcare practices, including dental implant decision-making. This study introduces a machine learning model utilizing the Classification and Regression Tree (CART) algorithm to estimate dental implant candidacy, drawing on anonymized patient records from Ellisa Dental Clinic, Batam. The dataset comprises various demographic and clinical attributes such as age, sex, smoking patterns, bone condition, and the presence of chronic illnesses including diabetes, hypertension, and autoimmune disorders. The exploratory analysis reveals that factors like heavy smoking, systemic diseases, and jawbone integrity substantially affect implant suitability. The quality and consistency of the dataset support robust modeling. The proposed system is intended to function as a clinical decision aid, offering dentists evidence-based recommendations regarding patient eligibility. This work demonstrates the potential of predictive analytics to enhance decision accuracy and streamline dental care, contributing to the integration of AI into routine clinical workflows.]]>
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