Early detection of brain-related diseases plays a crucial role in improving treatment outcomes, reducing mortality, and enhancing the quality of patient care. However, traditional diagnostic methods—such as manual MRI/CT scan interpretation and neurological assessments—are time- consuming, error-prone, and highly dependent on specialist expertise. To address these limitations, this study presents an Artificial Intelligence (AI)-based tool designed for the early detection and classification of multiple brain disorders, including brain tumors, stroke indicators, Alzheimer's disease patterns, and abnormal EEG activity. The proposed system integrates advanced deep learning techniques, including Convolutional Neural Networks (CNNs), hybrid feature extraction, and medical imaging analytics, to automatically identify subtle abnormalities that may be overlooked by human observation. A comprehensive dataset comprising MRI scans, CT images, and EEG signal recordings was used to train and validate the model. The images were preprocessed using noise reduction, skull stripping, normalization, and region-of-interest extraction to improve diagnostic accuracy. The model was trained using supervised learning and evaluated using performance metrics such as accuracy, sensitivity, specificity, precision, recall, and F1-score. Experimental results demonstrate that the AI tool achieves high accuracy in early-stage detection, outperforming conventional diagnostic methods and providing faster, consistent, and automated analysis. The system holds significant potential for use in hospitals, rural clinics, telemedicine platforms, and large-scale screening programs. It can support neurologists by acting as a decision- support tool, reduce diagnostic delays, and contribute to improved patient outcomes. Future work will focus on expanding the dataset, integrating real-time monitoring, and enhancing the system's capability to detect additional neurological disorders using multimodal data. Overall, the proposed AI tool demonstrates that artificial intelligence can be a transformative technology in the field of brain disease diagnosis and early prediction.