Data science is becoming increasingly important in the current day for the analysis of medical data in order to enable detection and classification for improved diagnosis. Large volumes of data typically require more processing time and hardware resources, which limits the ability to quickly classify X-rays and ECGs, and generate accurate, high-quality images. This gap can be filled by using QML approach to the medical field. This approach may lead to notable improvements in error rate prevention, execution efficiency, and parameter optimization. Quantum machine learning advances the future of medical image processing and gives important insights for the interdisciplinary study of quantum systems and medical imaging. QML-based predictive models examine patient histories, genetic information, and drug response to create highly accurate personalized healthcare plans. The most recent advancements in medical image classification and image synthesis utilizing quantum machine learning are examined in this work, with a focus on notable benefits, challenges, and possible future directions.