Brain tumors pose a significant challenge in medical diagnosis and treatment, requiring precise and efficient techniques for detection, segmentation, and classification. Recent advancements in artificial intelligence (AI), particularly machine learning and deep learning, have revolutionized neuro-oncology by enabling automated and accurate tumor analysis through medical imaging. This study explores the role of AI-driven approaches in brain tumor segmentation and classification. Various AI methodologies, including convolutional neural networks and transfer learning, have demonstrated superior performance in identifying tumor regions, estimating malignancy, and enhancing diagnostic precision. However, addressing some problems of these techniques, such as handling data imbalance and model interpretability, is crucial for integrating AI-based models into clinical practice. This paper provides an in-depth analysis of current AI techniques, performance evaluation metrics, and available datasets, offering insights into future directions for improving AI applications in brain tumor research.