<p>This paper proposes a new framework of hybrid deep learning for improving brain tumor classification process from MRI scans with enhanced precision. The new framework combines spatial capability of EfficientNetB0 for features extraction with sequence processing of HyperLSTM for processing variability of MRI complexity that causes significant difficulties for accurate classification process of tumors. EfficientNetB0 is employed for utilizing efficiencies of deep convolution neural network while HyperLSTM processes features in sequence for identifying temporal relationships required for interpreting tumor progression and behavior. In addition to improving predictive capacity of the model, the framework also consists of Random Forest as the employed classifier based upon features optimized in dimension with the help of PCA and improving accuracy and efficiency of the system in the process of computations. The state-of-the-art design of the model includes advanced preprocessing methods like extensive imaging augmentation for the purpose of mimicking varied imaging scenarios over the dataset. The employed model is tested on a Kaggle dataset with results of 99% for classification accuracy. The findings indicate significant improvements over existing processes with the prospect of this new process influencing clinical diagnosis processes with improved speed and reliability of tumor classification processes that could be employed for targeted treatment strategies.</p>