The study offers an examination in quantum machine learning methods, uses for forecasting heart disease condition. Recent years have seen a rise in interest in possibility for using quantum machine learning techniques in healthcare settings. This study aims to give a summary of recent advances in hybrid quantum-classical machine learning in prediction and early detection of heart diseases. This study explains the techniques, datasets, constraints, and possible future uses of state-of-the-art QML algorithms for illness classification and prediction. Introduced Quantum Random Forest (QRF), a novel hybrid quantum-classical model that aims to improve performance in order to fill the stated research gaps.