Diagnosing brain tumors is a complex task due to their intricate characteristics and variability in presentation. Timely and accurate detection plays a vital role in ensuring effective treatment and improving patient prognosis. This study presents the development of an automated system for brain tumor detection and segmentation using Convolutional Neural Networks (CNNs). The model is trained on annotated MRI datasets to distinguish between normal and tumorous brain tissues with high accuracy. The proposed approach involves a comprehensive pipeline that includes image preprocessing to enhance MRI quality, training a CNN-based model for tumor recognition, and applying post-processing techniques to refine the output. By automating the diagnostic process, the system aims to support radiologists by increasing accuracy, reducing diagnostic delays, and minimizing manual interpretation errors. Furthermore, the project incorporates various image processing techniques and data augmentation strategies to strengthen the model’s performance and generalizability across diverse imaging conditions. The result is an intelligent and accessible diagnostic tool intended to assist healthcare professionals in delivering more precise and efficient brain tumor diagnoses, ultimately contributing to better clinical decision-making and patient care.