Classical machine learning algorithms face computational limitations when processing high-dimensional datasets, while quantum computing offers theoretical advantages through quantum parallelism and entanglement. This study implements quantum machine learning algorithms including quantum support vector machines (QSVM), variational quantum eigensolvers (VQE), and quantum neural networks on IBM quantum processors. We evaluated performance on classification tasks using UCI machine learning datasets and synthetic high-dimensional data. QSVM achieved 94.7% accuracy on the Iris dataset using only 16 qubits, while quantum neural networks demonstrated quadratic speedup potential for certain problem classes. Current quantum hardware limitations and noise effects are analyzed, with projections for near-term quantum advantage in specific machine learning applications.