Heart disease remains one of the main causes of death globally, and successful management and treatment depend on early identification. Professionals are developing systems to detect heart disease that precisely recognise individuals with the most significant risk and provide suitable treatment to avoid premature mortality. To this end, a hybrid approach utilising machine learning is suggested for forecasting cardiac disease. In this paper, we present a hybrid machine learning (ML) model for detecting cardiac illness that incorporates logistic regression (LR), support vector machine (SVM), and artificial neural network (ANN) approaches. The hybrid model is intended to increase the separate algorithms’ accuracy and lower the possibility of misclassification. In order to train the model, the predictions generated by the three algorithms are merged and then subjected to a process of majority vote. Four factors are used to assess the model’s performance: accuracy, sensitivity, specificity, and f1-score. The results show that the hybrid model outperforms the individual algorithms, with an accuracy of 88%, a sensitivity of 92%, a specificity of 86%, and an f1-score of 90%.