The classification and identification of brain tumors have a significant impact on the early detection and treatment of conditions affecting the brain. In this paper, we present a large dataset that includes pre-examination information and classification labels for images of brain tumors. The dataset comprises numerous different brain scans that have all been categorized as either having tumors or not. The dataset’s pre-examination components are designed to offer vital statistical and textural information about the images of the brain that is useful in identifying tumor characteristics. To extract these properties, several preprocessing and picture analysis techniques are applied. The dataset’s class designations form the basis for classifying brain tumors. Researchers and healthcare professionals can utilize this dataset to develop accurate and efficient brain tumor classification models utilizing machine learning algorithms and statistical methods. The establishment of accurate and automatic diagnostic techniques can be made easier because of the availability of this dataset, which will greatly enhance research on brain tumors. By making it easier to assess and contrast various classification systems, it can also encourage scientific cooperation and the creation of cutting-edge methods for identifying brain cancer.