Over the past few decades, heart disease has seen significant growth among all ages and early prediction became necessary. Data mining and machine learning techniques are used to solve the prediction problem utilizing new approaches to supervised learning. The Internet of Medical Things (IoMT) emerged from the combination of multiple fields and machine learning. The goal of this research is to develop an adaptive model for predicting cardiac disease. We provide a ranking-based hybrid feature selection method for identifying essential characteristics. The model proposed in this paper employs a clustering method in conjunction with support vector machine (SVM) to save training time and eliminate classification errors, hence boosting the model’s performance and increasing its efficiency.