Brain cancer is among the most dangerous tumors for patients of all ages, and radiologists find it challenging to determine its grade in automated diagnosis and health monitoring systems aiding radiologists in performing better diagnostic analysis, numerous deep learning-based methods for Brain Cancer Classification (BTC) have recently been presented in the literature. In this paper, an Intelligent Fuzzy Based Approach is adopted for the detection of brain cancer. The input images are first pre-processed using the Weiner filter, then the segmentation of the brain cancer image is done used by the Fuzzy C Means algorithm. From each segmented tissue, related features are then extracted using the Gray Level Co-occurrence Matrix (GLCM) method, and the features are chosen using the adaptive threshold method. In in order to improve quality and accuracy. Adaptive Neuro-Fuzzy Inference System (ANFIS) are utilized for classification which in turn effectively determines the presence or absence of brain cancer. In this paper, the results are adapted from the MATLAB software.