An accurate and early brain tumor identification and classification system is essential for making a proper and on-time treatment decision. Manually, brain tumors can be diagnosed and classified by analysis of histopathological reports of biopsy samples, but it is time-consuming and has a high chance of wrong identification and classification, which may put the patient in danger. Recent research and developments in Artificial Intelligence, Machine Learning, and Deep Learning have started to play important roles in medical diagnosis and support doctors in making smart treatment decisions. Convolutional Neural Networks (CNNs) are a powerful deep learning-based technique that has recently been applied to the automatic detection and classification of brain tumors. In this literature review, we have reviewed the research papers related to automatic brain tumor identification and classification approaches based on deep learning. A lot of research based on DL has been done in the last few years to implement brain tumor classification and segmentation models with significant results. Some of the drawbacks may be found in various previously proposed methods. So, more improved deep learning models may be implemented in further research by comparing, changing, and combining the various previously proposed models.