A correct diagnosis of brain tumours is crucial to making an accurate treatment plan for patients with the disease and allowing them to live a long and healthy life. Among a few clinical imaging modalities, attractive reverberation imaging gives extra different data about the tissues. The use of MRI-Magnetic Resonance Imaging tests is a significant method for identifying disorders throughout the human body. Deep learning provides a solution for efficiently detecting Brain Tumour. The work has used MRI images for predicting the glioblastoma of brain tumours. Initially, data is retrieved from hospitals in form of an image database to continue with the brain tumour prediction. Pre-processing of dataset images is a mandatory step to enhance the accuracy and smooth line supplementary stages. The intensity value of each MRI (Magnetic Resonance Imaging) is subtracted by the mean intensity value and standard deviation of the brain region. Further, reduce the medical image noise by employing a bilateral filter. Further, the preprocessed medical images are used for extracting the radiomics features from images as well as tumour segmentation. Thus the work adopts the tumor is automatically segmented into four compartments using mutually exclusive rules using Modified Fuzzy C Means Clustering (MFCM). The clustering-based approach is very beneficial in MR tumour segmentation; it categorizes the pixels using certain radiomics features. The most important problem in the radiomics-based machine learning model is the dimension of data. Moreover, using a GWO (Grey Wolf Optimizer) with rough set theory, we propose a novel dimensionality reduction algorithm. This method is employed to find the significant features from the extracted images and differentiate HG (high-grade) and LG (Low-grade) from GBM while varying feature correlation limits were applied to remove redundant features. Finally, the article proposed the dynamic architecture of Multilevel Layer modelling in Faster R-CNN (MLL-CNN) approach based on feature weight factor and relative description model to build the selected features. This reduces the overall computation and performs long-tailed classification. This results in the development of CNN training performance more accurate. Results show that the general endurance expectation of GBM cerebrum growth with more prominent exactness of about 95% with the decreased blunder rate to be 2.3%. In the calculation of similarity between segmented tissues and ground truth, different tools produce correspondingly different predictions.