Deep learning algorithms have completely transformed medical diagnostics by enabling accurate and efficient identification of brain tumors. Brain disorders often arise due to increased in excessive and improper cell counts, which can damage neural structures and, in severe cases, lead to malignant brain cancer. The reducing mortality rates requires prompt intervention and early discovery. This research work presents an architecture of deep neural network specifically designed for the purpose of detecting brain tumors from MRI images. The proposed model is evaluated against existing research using a similar dataset to assess its effectiveness. For comparison, performance parameters including area under the curve (AUC), recall, accuracy, precision, and loss are used. According to experimental results, the suggested CNN model accomplishes 98.61% accuracy, 99.70% AUC, 99% of both precision and recall, and 0.41 loss in a data set of 3,264 MRI images. These findings indicate that the suggested model surpasses current models and provides a dependable and effective technique for prompt brain tumor diagnosis.