Heart failure is common and often fatal that occurs when the heart is not able to pump enough blood to fulfill the body’s needs. Early prognosis and cure of heart failure can significantly improve patient outcomes, as current techniques are not always precise or timely. Support vector classifiers (SVMs), have been used to predict heart failure, but they can be restricted by the size of data they can process and the precision by which they can analyze it. In this study, the focus is on the use of quantum machine learning algorithms, specifically quantum support vector classifiers (QSVMs), for heart failure prediction. QSVM is an ML algorithm that works on a quantum machine and comprises of the quantum mechanics principles. Comparison is done on the performance of QSVMs to traditional SVMs on a dataset of heart failure patients. QSVMs were able to predict heart failure with higher accuracy than traditional SVMs, particularly when dealing with large or complex datasets.