Brain tumor detection and classification is a critical task in medical imaging, as it plays a key role in early diagnosis and treatment planning. Magnetic Resonance Imaging (MRI) has emerged as one of the most reliable and non-invasive imaging techniques for detecting brain tumors due to its high resolution and ability to provide detailed images of soft tissues. However, manually analyzing MRI images to identify and classify brain tumors can be a complex and time-consuming process, requiring expert radiologists. To address this challenge, machine learning (ML) approaches have been increasingly utilized to automate the process of brain tumor detection and enhance the accuracy and efficiency of diagnosis.