Quantum machine learning, which depends on multi-qubit systems, plays a central role in advancing quantum computing capabilities. This study evaluates the effectiveness of Quantum Support Vector Machines (QSVMs) within the constraints of Noisy Intermediate-Scale Quantum (NISQ) devices, accounting for real-world technical challenges. Unlike classical SVMs that utilize radial basis function kernels, QSVMs employ quantum feature mapping to transform data into quantum states for kernel matrix construction. When applied to intricate or conceptually unique datasets, QSVMs show advantages over conventional SVMs, which often struggle with class separation due to limitations in classical kernel design. The method exhibits consistent performance across data types, especially in cases where clear decision boundaries minimize model overfitting. By implementing straightforward unitary transformations, quantum feature maps generate tunable kernels that optimize hyperplane configurations through regularization. Comparative analyses reveal QSVMs outperform classical counterparts in managing complex data patterns and achieving higher accuracy on benchmark datasets. The research underscores how quantum kernel techniques could resolve dimensional scalability issues in classical machine learning while distinguishing between datasets that appear identical in classical frameworks but exhibit quantum-level differences.