Support Vector Machine (SVM) is a supervised machine learning algorithm used for classification and regression tasks. It works by finding the optimal hyperplane that separates different classes in a dataset. The goal is to maximize the margin between the closest data points of each class, known as support vectors, which helps improve the model's accuracy. SVM can handle both linear and non-linear data by using kernel functions, which transform the input space into higher dimensions. This flexibility allows SVM to effectively classify complex datasets. It is widely used in various applications, including image recognition and text classification, due to its robustness and effectiveness.