NeuraScan is a full-stack AI-driven web application designed to classify brain tumors from MRI scans in real time. The model is trained using EfficientNet and implemented with PyTorch, enabling accurate multi-class classification across tumor types such as glioma, meningioma, pituitary, and no tumor. The application features a FastAPI backend that handles image preprocessing, model inference, and secure API interactions. On the frontend, it uses React 18, TypeScript, and TailwindCSS to deliver a smooth, responsive user interface. Animations and UI elements are enhanced with GSAP, Lucide Icons, and Shadcn UI components. To enrich user experience with interpretability, the system integrates Google Gemini to generate AI-powered medical insights and explainable reports based on classification results. The platform emphasizes: 1. Real-time MRI image classification 2. Confidence scoring for each tumor type 3. AI-generated medical recommendations 4. Educational utility in healthcare research and pre-diagnostic support All data is processed in-memory with no storage, ensuring user privacy. The system is CORS-protected, API key–secured, and production-ready. This project demonstrates expertise in deep learning, medical imaging, model deployment, full-stack development, and real-time AI applications in healthcare.