Recent advancements in Artificial Intelligence (AI), machine learning (ML), and Deep Learning (DL) have significantly impacted healthcare diagnostics. The Health Care Diagnostic Pod exemplifies this transformation by integrating various diagnostic tools such as blood pressure monitors, glucometers, and ECG machines into a compact device connected to a central health repository via Wi-Fi. The proposed system employs Quantum Machine Learning (QML) algorithms to enhance diagnostic accuracy and efficiency. This work utilizes cardiac disease prediction using Heart disease prediction dataset as a study to demonstrate the effectiveness of QML in this Pod. The proposed work incorporates three machine learning models: Quantum Support Vector Machine (QSVM), Classical Support Vector Machine with Quantum Kernel (CSVM-QK), and Classical Support Vector Machine (CSVM). The CSVM achieved an accuracy of 86%, while the CSVM-QK improved to 96%. The QSVM demonstrated the highest accuracy at 98%. Comparative analysis revealed that while QSVM took longer to train, it significantly reduced testing time and overall execution time compared to classical methods. These findings suggest that QML can provide superior accuracy and efficiency in real-time medical diagnostics.