This study presents a quantum machine learning framework for improving cost variance analysis in industrial manufacturing. It addresses the challenges posed by classical methods when handling high-dimensional and nonlinear industrial process cost data. By integrating Quantum Support Vector Machines (QSVMs) with quantum kernel methods, the framework uses quantum-enhanced feature mapping to transform cost data into a high-dimensional Hilbert space where complex patterns become more separable. A rigorous numerical experiment is conducted using a synthetic dataset that simulates real-world manufacturing cost variations, including price, efficiency, and volume variances. The results indicate that while a classical SVM with a radial basis function kernel failed to detect anomalies, the QSVM achieved a precision of 21%, a recall of 40%, and an F1 score of 27%, along with a shorter training time. These findings validate the hypothesis that quantum approaches can effectively capture nonlinear interdependencies among cost drivers and improve anomaly detection. Despite promising performance, the study acknowledges current limitations in quantum hardware scalability and interpretability. Overall, this work not only advances the theoretical understanding of quantum machine learning for cost analysis but also lays the groundwork for future practical implementations in process optimization and decision support within complex industrial environments.