A brain tumor (BT) originates from the abnormal and rapid proliferation of tissues. The evaluation and forecasting of brain tumors can be improved with prompt and precise examination using magnetic resonance imaging. Automated diagnostic methods must recognize, segment, and classify medical images to assist specialists in effectively diagnosing brain tumors. The manual identification of tumors is a laborious and error-prone task for clinicians; thus, the use of an automated approach is essential. The identification of tumors is significantly impeded by differences in their location, shape, and size. Numerous scientific investigations have already explored BT detection. This paper presents a comprehensive assessment of contemporary methods for BT recognition, segmentation, and classification employing machine and deep learning techniques. The study examines the various types of datasets employed for brain tumor detection and the associated evaluation metrics. This paper intends to provide a comprehensive evaluation of deep learning models for brain tumor segmentation and classification, including advanced approaches and their effectiveness. Many studies have attained. Many studies have achieved accuracy over 95% in BT detection with DL models. This paper also delineates research difficulties and potential possibilities for brain tumor identification to aid present investigators and practitioners. This study will enable readers to identify a suitable approach for suggesting enhanced methods for brain tumor identification. The suggested approach integrates CNNs, Transformers, GAN-based augmentation, and multimodal MRI sequences for BT detection and classification.