A key component of quantitative brain image interpretation is the precise classification of brain tumors in magnetic resonance (MR) images, which has attracted a lot of scientific interest. Conventional techniques for segmenting MR brain images, including K-means and fuzzy C-Means (FCM) algorithms for clustering, handle every pixel independently and do not integrate spatial information between nearby pixels. Therefore, the noise and degree of homogeneity in brain magnetic resonance imaging poses challenges to the precision of these classification methods. We present a novel way to classification that tackles this problem by utilizing the hybrid shaft clustered technique, that blends Fuzzy Kernel C-Means (FKCM) and adaptive K-means clustering. Furthermore, we determine the area by figuring out how many cells the tumor occupied and how segmented its area is. The results of our simulation show that, in comparison to traditional methods, our suggested method delivers greater segmentation and area estimating reliability.