This research introduces a multimodal AI-powered tutoring system designed to make learning more personalized, engaging, and accessible. Unlike traditional digital platforms that provide generic responses, the proposed tutor adapts to each student by understanding their study material and learning needs. It uses low-latency Large Language Models combined with Retrieval-Augmented Generation (RAG) to answer questions directly from uploaded PDFs, ensuring clarity and accuracy. Visual-AI modules interpret diagrams and images to support visual learning, while an automated assessment engine generates MCQs for instant performance feedback. Results show that this approach boosts student confidence, comprehension, and self-directed learning, helping reduce educational gaps and making high-quality tutoring available to everyone.