Traditional methods for the categorization of brain tumors identification and mostly depend on radiologists' time-consuming manual interpretation and subjective. Detecting a brain tumor in its early stages can be quite very difficult for doctors. Traditional methods for the categorization of brain tumors identification and mostly depend on radiologists' time-consuming manual interpretation and subjective. This process can be subjective, complex and laborious. Recent developments in deep learning have greatly improved the automation and accuracy of medical image processing, including the categorization and identification of brain tumors from MRI scans. The study emphasizes how crucial it is to solve MRI IMAGE dataset: A variety of datasets, including medical datasets, are hosted by Kaggle, a platform for data science competitions (Kaggle offers datasets related to brain tumors at ( https://www.kaggle.com/datasets ) in MRI image constraints, optimize segmentation algorithms, and incorporate cutting-edge methods like data. MRI pictures may be more susceptible to noise and other environmental disruptions. Doctors find it more challenging to diagnose the tumor and its sources as a result. In order to overcome this, we created a method that uses grayscale image conversion to identify brain tumors from images. To remove noise and other distracting elements from the image, we employ filters. The system's processing will include a preprocessing step for the selected image. To find brain cancers in the MRI pictures, multiple algorithms are applied at the same time. However, the edges of the image may not be crisp and distinct in the early stages of a brain tumor. As a result, to identify the edges in the pictures, we are using image segmentation. We have developed a number of filtering algorithms and segmentation procedures to extract information from photos. Precision can be increased overall with this strategy.