The diagnosis of brain tumors is a crucial undertaking in the field of medical imaging, necessitating the utilization of precise and dependable methodologies to facilitate early identification and intervention. In recent times, the utilization of Support Vector Machines (SVMs) has been recognized as a viable methodology for the automated detection of brain tumors. Support Vector Machines (SVMs) represent a robust machine learning method that exhibits notable efficacy in classification tasks. In comparison to alternative machine learning algorithms, including neural networks and decision trees, SVMs provide distinct advantages. This manuscript provides a comprehensive overview of recent scholarly investigations pertaining to the application of Support Vector Machines (SVMs) in the context of brain tumor diagnosis. The present study encompasses an examination of the benefits associated with the utilization of Support Vector Machines (SVMs) in the context of brain tumor identification. Additionally, it addresses the difficulties that arise in the process of selecting appropriate SVM parameters, as well as the rates of accuracy reported in various researches. The findings indicate that Support Vector Machines (SVMs) have demonstrated notable levels of accuracy, ranging from 90% to 95%, as reported in several research publications. Nevertheless, it is imperative to exercise caution while selecting parameters in order to attain the most favorable performance outcomes. Additional investigation is required in order to substantiate the efficacy of brain tumor detection techniques based on Support Vector Machines (SVM) when applied to more extensive datasets. Furthermore, it is imperative to tackle the obstacles related to the selection of SVM parameters. In summary, the utilization of Support Vector Machine (SVM) for brain tumor identification exhibits considerable potential in enhancing the precision and efficacy of brain tumor diagnosis.