Brain tumors can vary from relatively benign to very malignant and are a major health threat. Early detection as well as accurate diagnosis is important to enhance the treatment efficiency and increase the survival rates. Although traditional imaging methods such as MRI and CT scans are the standard, they rely greatly on the ability of radiologists. This dependence can render the process time-consuming and somewhat arbitrary, which is especially undesirable in regions with scarce medical resources, where uniform diagnoses are harder to achieve. ML is transforming this field by providing faster and more reliable means for detecting brain tumors. ML-based systems use feature extraction, where patterns in medical images are identified as tumor indicators. Various techniques like CNN and autoencoders enhance the efficiency and precision of the procedure. After feature extraction, classification of tumor can be done using technique like support vector machines (SVMs) and neural networks and these models also specify the nature of tumor, e.g., gliomas or meningiomas, by giving appropriate training with properly annotated datasets. Still, some challenges remain. High-quality, labeled clinical data are available in limited supplies, and the models tend to run into issues such as overfitting or compromised performance upon deployment on diversified patient populations. Issues of bias and the uninterpretable character of some ML models also cloud clinical deployment. In future, the incorporation to imaging data with genomic and the clinical data, enhancing model accuracy, and solving ethical problems will be essential. With these advances, ML has the potential to greatly enhance brain tumor diagnosis and provide high-quality care more universally.