The MRI brain image detection and classification is very crucial for medical science. From last few years, various methods are being presented. Here in this paper, we proposed a novel approach to classify a given MRI brain image as normal or abnormal i.e. affected through tumor. The performed method firstly taken the wavelet transform for feature extraction from the images then applied principle component analysis to decrease the dimensions of features. Then the decreased features are being employed to kernel-support vector machine (k-SVM). Here the data has been taken for seven general brain diseases viz. glioma, Alzheimer's disease, Sarcoma, coma, LINN, IPOL, HPOL. We collected the data of 20 normal and 140 abnormal patients from Harvard Medical Laboratory website. Here four methods of SVM are performed such as L-Kernel, RBF, Poly, and NN. The comparative analysis is also being performed by the existing literatures which illustrates the result by DWT+PCA+KSVM by applying with GRB Kernel. Our classification gives fast and accurate results and assist doctors to distinguish between normal or abnormal to certain precision values.