The human heart is a vital organ, playing a crucial role in the body's functioning. Heart disease, a condition affecting the heart, is globally recognized as the leading cause of mortality, resulting in a significant number of deaths. Over the past few years, there has been a significant increase in the global incidence of cardiovascular disease. Several problems that are classified under the broad category of “heart disease” include myocardial weakness, arrhythmia, cardiac ailments, and coronary artery disease. The latter condition has the potential to cause a reduction in blood flow to the heart, leading to the occurrence of a myocardial infarction. This necessitates a medical diagnosis. The accurate prediction of cardiovascular disease poses significant challenges within the realm of therapy. Using machine learning to classify cardiovascular disease occurrence can help diagnosticians reduce misdiagnosis. Thus, this research develops a model that can correctly predict cardiovascular diseases to reduce the fatality caused by cardiovascular diseases. This study provides insight into the potential of several ML techniques as an effective tool for predicting heart failure disease and highlights the decision tree algorithm as a potential option for further research. The proposed algorithm was validated using a widely used open-access UCI Heart Disease Dataset sourced from UCI Repository, where 10-fold cross-validation is applied in order to analyze the performance of heart disease detection. An accuracy level of 98.87% accuracy was found employing the Gradient Boosting Tree algorithm along with precision and recall of 96.63% and 95.05% respectively.