Brain tumors are abnormal formations of brain cells that may be benign or malignant. The diagnosis of the disease may involve imaging studies or biopsy. Therapy involves surgical procedures, radiation treatment, and radiotherapy, depending on the type and location of the tumor. Progress in identifying and treating brain tumors is essential for enhancing the prognosis and well-being of individuals impacted by them. Brain tumors need to be diagnosed promptly and accurately through the use of medical imaging techniques. Nevertheless, intrinsic noise diminishes the quality of images and hinders their usefulness for diagnosis. This study is centered around the creation and assessment of cutting-edge denoising methods to enhance the precision of brain tumor identification in medical scans. In our research, we utilize the bm3d filter for noise reduction in brain MRI images and a two-layered CNN model for brain tumor detection. By systematically evaluating the effectiveness of this method, we determine the most optimal approach to reduce noise disturbances while retaining critical diagnostic data. We are studying how reducing noise is able to affect accuracy and enhancing the entire process to enhance clinical decision support. The findings of this study are expected to enhance the precision of brain tumor detection, leading to far more dependable and effective healthcare procedures. The results could allow traditional medical imaging procedures to integrate sophisticated denoising methods, leading to improved diagnostic abilities for neuroimaging experts in the future.