Machine learning (ML) has revolutionized numerous fields by providing sophisticated tools for data analysis and predictive modeling. Among these tools, Support Vector Machines (SVMs) stand out due to their strong theoretical foundations and practical efficacy. SVMs are particularly well-suited for tasks involving classification and regression, where they excel at finding the optimal hyperplane that separates data points of different classes with maximum margin.This paper aims to provide an in-depth review of SVM applications in various research areas, supported by an examination of relevant literature. The integration of SVMs with other advanced methodologies, such as deep learning and fuzzy systems, will also be discussed to demonstrate their versatility and potential for further innovations.