Purpose <p>This innovation aims to assist specialists in selecting appropriate treatments to enhance patient outcomes and prolong their lives. Initially, the required 3D MRI is synthetically initiated by the Attention-based Stacked Conditional Generative Adversarial Network (A-SCGAN). The synthetically generated images are used to mitigate class imbalance in the brain tumor detection process. The synthetically generated 3D images are passed to the segmentation phase, leveraging with 3D Trans Dilated Mobile Unet (3D-TDMobileUNet). The segmented images are given to the Adaptive MobilenetV2 (AMV2) model for classifying the brain tumor into various classes. Here, the parameter tuning takes place via Enhanced Exploration-based Carpet Weaver Optimization (EECWO) to enhance tumor classification performance. Experimentation is conducted on synthetically created 3D images and after generating 3D images, the quality of synthetically generated images is compared over the original images to prove the same.</p> Methods <p>The tumors identified in the segmented images are classified into discrete types with the support of the AMV2 model. This proposed model utilizes depth wise separable convolutions, which perform a filtering process to extract relevant features and then produce classified outcomes depends on these features extracted. The developed AMV2 model achieves high classification accuracy, especially when trained on well-prepared datasets that reflect the variations of the MRI images.</p> Results <p>The accuracy of the developed EECWO-MV2 model reached 93.1% that was greater than traditional models like ResNet, CNN, VGG16, and MV2 at a hidden neuron count of 200 using Dataset 1.</p> Conclusion <p>Thus the proposed approach proved as a important advancement in the medical field, facilitating timely and accurate diagnoses that could ultimately enhance patient care.</p>