Intracranial hemorrhage (ICH) is one of the severe types of brain stroke. Artery burst results in bleeding inside the brain and its surrounding tissue. Depending upon brain bleed location, hemorrhage gets classified. This paper presents a hybrid feature selection approach to form joint feature vector sets using transformed image features and image-based gray level co-occurrence matrix (GLCM) texture features. Feature extraction is performed by applying discrete wavelet transform, discrete cosine transform, and GLCM features. Feature vector selection model is built with the combination of Discrete wavelet transform, Discrete cosine transform, and GLCM features. A joint feature vector is formed with transformed image features and GLCM features. These joint feature vectors form an acute input for the further classification process. The machine learning algorithm i.e. Random Tree, Random Forest, and REPTree are used for the classification of Intracranial hemorrhage CT images. The classification results obtained are further analyzed for accuracy considering transformed features and GLCM features. It is observed that Random forest classifier results in the highest classification accuracy of 87.97% for discrete wavelet transform and GLCM (DWT+GLCM) feature set