The classification of brain tumors using deep learning algorithms has emerged as a crucial area of research in medical image analysis. This review provides a comprehensive overview of the application of deep learning techniques in the field of brain tumor classification. Deep learning algorithms, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), have demonstrated remarkable performance in accurately diagnosing brain tumors from medical imaging data, including MRI and CT scans. This review synthesizes the key findings from recent studies, highlighting the advancements in model architectures, data preprocessing, and performance evaluation metrics. The paper also discusses the challenges and limitations faced by these algorithms, such as the scarcity of labeled data, model interpretability, and generalization to diverse patient populations. Furthermore, it explores potential future directions in the field, including the integration of multi-modal data and the incorporation of explainable AI techniques to enhance clinical adoption. Overall, this review serves as a valuable resource for researchers, clinicians, and policymakers interested in the intersection of deep learning and brain tumor classification, offering insights into the current state-of-the-art and avenues for future research.