Support vector machines (SVMs) are widely utilized in health care research for tasks such as classification, regression, and outlier detection. These models function by developing hyperplanes that maximize the separation between different classes in a feature space, enabling accurate prediction and classification. SVMs are classified into linear, nonlinear (eg, kernel-based), and multiclass variations. Several orthopedic and plastic surgery studies have found success in using SVMs for diagnosis and outcome prediction. While their robustness makes them effective for high-dimensional datasets, SVMs are not without limitations, and future work will be of benefit to strengthen an already powerful and popular technique.