In the era of rapid technological advancement, the growing focus on innovation can sometimes overshadow the critical importance of healthcare. With an increasing global population and limited healthcare resources, early diagnosis and treatment have become essential, making this a pivotal area of research. While Classical Machine Learning (CML) has been a prominent solution, the surge in nonlinear and heterogeneous healthcare data presents limitations that quantum machine learning (QML) can overcome. QML leverages the principles of superposition and entanglement, enabling faster and more accurate data processing compared to CML. By utilizing qubits instead of classical bits, QML opens new possibilities for handling complex datasets with greater efficiency and precision, which is particularly relevant in today’s fast-paced digital age where quick, reliable decision-making is crucial. This paper reviews the potential of QML in healthcare analytics, exploring how quantum algorithms can address challenges in early diagnosis, drug discovery, and personalized medicine. The review also highlights the advantages of QML’s higher complexity tolerance, which allows for improved analysis of intricate healthcare data. As the first comprehensive review on this topic, it not only investigates current applications of QML but also offers insights for future advancements, marking a significant milestone for both the healthcare and technology sectors.