Cardiovascular disease is currently a prominent sickness that kills the majority of sufferers. The medical evaluation of cardiac disease presents significant problems. This diagnostic method is complex, requiring precision and efficiency. Early detection of heart disease can significantly lower the chance of death. In light of the high occurrence of cardiac issues in contemporary times, the prediction of heart disease has emerged as one of the most challenging endeavors within the medical field in recent years. Scientists examined a plethora of closely related characteristics to identify the most trustworthy predictors of these diseases. In this work, we use Machine Learning (ML) approaches to detect the existence of cardiac problems. The suggested method predicts the likelihood of heart disease and classifies people into risk categories. This is performed by utilizing several machine learning algorithms such as Support Vector Machine (SVM), Gradient Boosting, Random Forest (RF), K-Nearest Neighbors (KNN), Naive Bayes (NB), and Logistic Regression (LR). The developed system is trained and evaluated using a composite dataset derived from two separate sources. The results of the experiments show that, when compared to other machine learning algorithms, the Random Forest approach achieves the greatest accuracy rate, achieving an amazing 99.99%.