<p>Brain tumors are among the deadliest disease that affects the life of the human. One of the deadly diseases that affect the human population is the. It is a condition due to the damage that occurs in the chromosomes of particular cells or genes of the brain and due to this the brain cells halt its function. The techniques that are commonly utilized for the identification of brain tumors are Deep Learning, Computer-Aided Diagnosis, and Artificial Intelligence. Many of the existing techniques faced challenges in the accurate segmentation of various brain tumors. Hence, the 3D attention convolutional-based cross-modal distillation (3DAC-CMD) method is proposed to enhance automatic identification and MRI-based brain tumor segmentation. The datasets that are involved in the validation of the proposed method are Figshare Brain Tumor and Brain tumor MRI. Data preprocessing is carried out to facilitate training of the model by applying noise removal, normalization, contrast enhancement, image resizing, and image flipping. To transfer knowledge from one to other modalities, cross-model distillation is implemented. For a more precise estimation of brain tumor segmentation, a 3-dimensional Convolutional Neural Network is utilized. Attention Mechanism is employed to particularly focus on extracting information from area of interest. The existing methods utilized to compare the effectiveness of the proposed method are the AS-COA-E-U-Net, CJHBA, TISS-Net, RAAGR2-Net, and DenseUNet + . From the evaluations, it was found that the 3DAC-CMD method attained the highest performance metric values of 0.97 dice coefficient and 98.5% precision. From these values, it is revealed that the effectiveness of the 3DAC-CMD method is better than the existing methods and proved its effectiveness.</p>