Support Vector Machines (SVMs) are powerful tools for classification and regression in machine learning. This article explores their geometric intuition, mathematical foundations, and use of kernels for handling non-linear data. It also covers Support Vector Regression (SVR), key applications across domains, strengths, limitations, and practical tips—offering a comprehensive, accessible guide to mastering margins.