This research work handles large volumes of data, complex nondeterministic data, and extensive data exploration better than humans. Deep learning for medical image analysis can help discover lesions, diagnose patients, reduce clinician effort, reduce medical errors, and improve diagnostic and prognostic outcomes. Deep learning’s cancer diagnosis benefits have attracted many researchers. Advances in technology offer opportunities for deep learning in medical image analysis. The benefits of deep learning in brain cancer diagnosis have also gained interest from many researchers. Predicting malignancies from three-dimensional (3D) medical pictures using the existing deep learning model is difficult due to greater prediction cost, computational complexity, lesser training samples, overfitting, and worse prediction accuracy. Diffusion tensor imaging (DTI), CT, functional MRI (fMRI), MRI, and ultrasound scans may now be analyzed using 3D deep learning. These scans show organs in three dimensions and can detect infections, cancers, serious injuries, and blood artery and organ irregularities. These are the abilities that inspired us to introduce unique 3D medical image-based deep learning algorithms for smart cancer prediction in this study. The predictive score achieved in this study demonstrates the model’s effectiveness in accurately diagnosing cancer.