Quantum technology has become a revolutionary paradigm that can be used to transform data processing according to the principles of entanglement and superposition. Although it has theoretical prospects, there are presently limited practical applications in machine learning in practice due to circuit depth, noise sensitivity, and limited qubit fidelity, which limit scalability and stability. Meeting these challenges, the current study applies and compares a common framework that incorporates both traditional machine learning and quantum machine learning models for comparative performance evaluation. The models under examination are Support Vector Machine (SVM), Convolutional Neural Network (CNN), Quantum Neural Network (QNN), Quantum Support Vector Machine (QSVM), and Quantum Convolutional Neural Network (QCNN). Simulations were performed using Python with TensorFlow and Keras for traditional implementations and Qiskit for quantum simulation on the MNIST multi class image dataset and the UCI Breast Cancer binary classification dataset. The outcomes illustrate that SVM recorded 98% accuracy in MNIST and 97% in UCI with an AUC score of 1.00, establishing its efficiency and computational power. QCNN attained 96% and 97% accuracy with AUC scores of 0.99 and 0.98 respectively, demonstrating higher discriminative power at the cost of almost five times higher training time owing to circuit complexity. QSVM reached 90% accuracy, indicating sensitivity to feature map architecture and kernel setup. The comparative analysis shows that although SVM and CNN are still more effective under existing hardware conditions, quantum models, especially QCNN, have higher representational capability and scalability potential. The current study presents a reproducible basis for building hybrid quantum classical architectures for improving accuracy, flexibility, and computational efficiency in biomedical and image classification domains.