Support Vector Machine (SVM) is a powerful supervised machine learning algorithm primarily used for classification and regression tasks. It operates by finding the optimal hyperplane that separates data points of different classes in a high-dimensional space. SVM is particularly effective in handling non-linear decision boundaries through a technique known as the kernel trick, which transforms the input data into a higher-dimensional space. This allows SVM to create complex models while maintaining robustness against overfitting. Its applications span various fields, including image recognition, bioinformatics, and text classification, making it a versatile tool in the machine learning toolkit.