Brain tumors are among the most critical neurological disorders and require early and accurate diagnosis to improve patient survival rates. Traditional methods of tumor detection rely heavily on manual analysis of medical images such as Magnetic Resonance Imaging (MRI), which can be time-consuming and prone to human error. This study presents a machine learning–based approach for the automated detection and classification of brain tumors from MRI images. The proposed system utilizes image preprocessing techniques to enhance image quality and remove noise, followed by feature extraction to identify significant patterns associated with tumor regions. Various machine learning algorithms, such as Support Vector Machines (SVM), Random Forest, and Convolutional Neural Networks (CNN), are applied to classify MRI images into tumor and non-tumor categories, and further categorize tumor types. The model is trained and evaluated on a labeled MRI dataset to ensure accuracy and reliability. Experimental results demonstrate that the proposed method improves diagnostic efficiency and achieves high classification accuracy compared to traditional approaches. This automated system can assist radiologists and healthcare professionals in early tumor detection, reducing diagnosis time and improving treatment planning.