The human brain stands as the master controller of the intricate humanoid system. Anomalous cell proliferation within this vital organ can give rise to brain tumors, eventually progressing to the dire state of brain cancer. Within the domain of human health, the integration of Computer Vision emerges as a formidable ally, mitigating human error and furnishing precise diagnostic outcomes. Among the array of imaging modalities, including CT scans, X-rays, and MRI scans, magnetic resonance imaging (MRI) reigns supreme for its reliability and safety. Notably, MRI possesses the remarkable ability to discern even the most minute anomalies. Our research endeavors to explore diverse methodologies aimed at detecting brain cancer through the lens of brain MRI. Our approach involves meticulous preprocessing steps, employing techniques such as bilateral filtering (BF) to eliminate image noise, alongside binary thresholding and Convolution Neural Network (CNN) segmentation to accurately delineate tumor regions. Through the utilization of comprehensive training, testing, and validation datasets, we aim to leverage machine learning algorithms to predict the presence of brain tumors in subjects. Evaluation of our methodology will be conducted using a suite of performance metrics, encompassing accuracy, sensitivity, and specificity. It is our fervent aspiration that our proposed framework will outshine existing methodologies in the field, offering a novel and superior approach to the detection of brain cancer.