With the widespread application of machine learning techniques in various fields, this research explores the application of Support Vector Machine (SVM) in teacher performance evaluation. Teacher performance evaluation is an essential aspect of educational management, and traditional evaluation methods often rely on student feedback and subjective judgments from administrators, potentially lacking objectivity and consistency. To overcome these limitations, this study utilizes the SVM algorithm, a powerful supervised learning model, to analyze and predict teacher performance. We first collected data, including factors such as teaching quality, student feedback, peer evaluations, and other relevant metrics. Then, we used this data to train SVM models to identify key factors influencing teacher performance and make effective predictions. Experimental results demonstrate that SVM exhibits higher accuracy and reliability in teacher performance evaluation compared to traditional methods. This research not only demonstrates the potential application of SVM in the field of education but also provides a new and more scientific approach to teacher performance evaluation.