Brain tumors are when cells in the brain grow abnormally and can be either non-cancerous or cancerous. These growths interfere with brain function and present symptoms such as headaches, seizures, memory loss, and cognitive challenges. Detection of them early is crucial for treatment. Current diagnostic techniques are time-consuming and heavily reliant upon radiologists. Errors in interpretation or delays in diagnosis could result in outcomes underscoring the importance of developing efficient diagnostic tools. With the advancements, in intelligence and deep learning technologies, today comes a rising demand for automated tools to aid in the detection of brain tumors efficiently and effectively. These systems can offer swift results to healthcare professionals for making informed decisions. The model presented in this context leverages learning methodologies like Convolutional Neural Networks (CNNs) to automate the identification of brain tumors from MRI scanned images. By analyzing a labeled dataset of MRI images and extracting features from them the model categorizes the scans into either being positive for a tumor or negative.