<p>Because brain tumors proliferate uncontrollably inside the cranial cavity and cause serious neurological consequences, they present severe hurdles for both diagnosis and treatment. This research delves into the interdisciplinary approach necessary for efficient treatment planning, entailing cooperation between neurosurgeons, oncologists, and additional experts. With further improvements in advanced imaging technologies and molecular diagnostics, the key to better patient outcomes is still detection and diagnosis. Even then, brain tumors represent a complex entity, the benefits from which can accrue only after incessant studies and development. In this work, subjects were categorized by type of brain tumor with the application of 2 very popular machine learning (ML) models: Decision Trees Classifier (DTC) and Support Vector Classifier (SVC). Further, optimization of these models was performed with the use of sophisticated optimization methods, namely the Differential Squirrel Search Algorithm (DSSA) and the Escaping Bird Search for Constrained Optimization (EBSO), so that these models could work out their best regarding accuracy and dependability. The best models identified were SVDS, SVEB, DTDS, and DTEB, which were used in the analysis. The model that achieved the highest accuracy is the DTEB model, in which all the data analyzed had an accuracy of 0.969. It is also the most robust model in this study because it showed the highest accuracy in both phases, training, and testing, which were 0.970 and 0.969, respectively. The application of advanced optimization techniques and ML models is indicative of the commitment to the utilization of the latest techniques for medical diagnosis and decision-making. This project tries to increase the accuracy and efficiency of diagnosing brain tumors by incorporating novel optimization approaches combined with well-established classification algorithms. Ultimately, this would translate to better patient outcomes and more personalized healthcare interventions.</p>