A brain tumor is a dangerous cancer that develops when cells divide uncontrollably and abnormally. Recent advancements in deep learning have aided the medical imaging industry in diagnosing various disorders medically. Convolutional neural networks are the most often used machine learning algorithm for visual recognition and learning. Additionally, we demonstrate by using CNN to classify brain MRI images into two categories: cancer and non-cancer. Using the transfer learning method, we evaluated our convolutional model’s performance to previously trained ResNet-v2-152, Inception-v3, and Inception-Resnet-v2 models. As a result of the experiment, a moderate dataset was used. However, the test result indicates that the suggested model’s accuracy was adequate, reaching 99 percent, compared to 98 percent for ResNet-v2-152, 98 percent for Inception-v3, and 97 percent for Inception-Resnet-v2. The suggested model requires far less computational resources and is more efficient.