Brain tumor diagnosis is a critical area of medical research as it directly impacts patient survival and treatment. Early diagnosis is essential for improving prognosis and facilitating therapy. Magnetic Resonance Imaging (MRI) is particularly reliable for detecting brain tumors due to its superior image quality and contrast. This study provides a comprehensive review of recent advancements in brain tumor diagnosis methods. Evolutionary algorithms based on natural selection principles offer optimal strategies for image processing, segmentation, feature extraction, and classification in tumor diagnosis. Random Forest algorithms are used in this study to classify MRI images, distinguishing between normal brain tissues and malignancies. Additionally, the study explores hybrid models that integrate evolutionary algorithms with neural networks (CNN) to enhance accuracy. This research offers insights into the benefits and limitations of these approaches, paving the way for further neuropsychology research.