Dental implants success poses a significant challenge in oral rehabilitation, influenced by various patients-specific and surgical factors. The objective of this study is to employ supervised machine learning (ML) algorithms to predict dental implant failure emphasizing on feature interaction analysis and its impact on model accuracy and reliability. 747 implants record dataset was utilised consisting of features related to patient surgeon and surgery. Features were selected based on statistical test and backward elimination. Greenwell's method and Freidman's H statistical method were employed to identify interacting pairs, followed by modelling with Random Forest, K Nearest Neighbor and Extreme Gradient Boosting with hyper parameter tuning via Standardized Search CV and Grid Search CV. While some interacting pairs improved model accuracy marginally, not all interactions led to improvement. With 'ridge augmentation and age' interacting pair, XG Boost achieved the highest accuracy of 89.04%. However, it could not surpass the Random Forest accuracy of 90.4% achieved through feature selection and hyperparameter tuning without interactions. The study demonstrated the potential oi feature interaction modelling in predicting dental implant failure but emphasized the careful selection of interactions. Future research can refine the understanding of feature interactions and its impact on predictive models, leading to enhanced clinical decision making in dental implantology.