Classification of brain tumors is the most important thing in the diagnosis process with treatment of brain tumors, playing the biggest role in the treatment process as well as the prognosis of patients. Brain tumors could be benign or malignant and show extensive variability in their morphological as well as biological characteristics; they require a precise and timely diagnosis. Traditional diagnostic methods are mainly based on histopathological studies, which although they are definitive, are quite invasive and time-consuming. Recent developments in medical imaging technologies, including Positron Emission Tomography (PET), Computed Tomography (CT), and Magnetic Resonance Imaging (MRI), have completely transformed the field by providing real-time, noninvasive evaluation of tumor features. Here, image fusion plays a very important role in analyzing the image characteristics. This paper included some fusion methods and classification algorithms. It covers the various classification algorithms and fusion methodologies. This research focused on brain tumor classification based on MRI images. Intermediate fusion techniques, particularly those employing deep learning, consistently offer the best performance.