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Brain tumor identification and classification by the application of deep learning and sophisticated image-processing techniques have been receiving a lot of attention in the medical field. Accurate diagnosis, treatment planning, and patient outcome rely on the accurate identification and differentiation of brain tumors, such as gliomas, meningiomas, and metastases. The most recent developments in deep learning-based techniques for brain tumor identification and categorization by applying medical imaging data, including Computed Tomography (CT). This study looks at CT and Magnetic Resonance Imaging (MRI). The main topic of this study is the general theory and developments in deep learning methods for the identification and classification of brain tumors. It investigates how the precision and efficacy of tumor detection can be increased through a variety of models, including Generative Adversarial Networks (GANs) and Convolutional Neural Networks (CNNs). The paper emphasizes the Significance of applying novel deep-learning techniques for facilitating precise and automated brain tumor identification for individualized treatment, with an emphasis on important achievements, challenges, and opportunities in the area.