The ever increasing size as well as complexity of high-dimensional databases in the medical field leads to severe difficulties on binary classification of the diseases say pneumonia or Tuberculosis. Classical machine learning models are useful but come short on issues of scaling, interpretability and feature selection when dealing with large databases. This new development in quantum computing lets people access advanced dimensional data processing as well as optimization processes which is commendable to say the least. The research work presents Hybrid Quantum-Classical Algorithms the work that integrates quantum-assisted feature selection, kernel-based learning and classical machine learning models in order to improve predictive accuracy, scalability, and interpretability. Using, for instance, the Quantum Approximate Optimization Algorithm (QAOA) optimizes the feature selection process by efficiently identifying important features, transforming them into relevant quantum states, and obtaining appropriate quantum kernels for classification task. These hybrid models evaluate data retrieved from clinical settings such as chest X-ray and genomic data, and compare performance on these datasets with conventional SVMs and neural networks. The results of the experiments emphasize the increased accuracy of predictions, ability to scale with larger datasets, and the ability to interpret the model via feature importance and decision graphs. This research integrates quantum and classical computing to address the gap between quantum computing promise and real-world healthcare applications by enabling a cost-effective, interpretable, and robust strategy for analyzing the medical datasets.