The RAG-Based Intelligent Document Question Answering System is developed to make searching information from large documents easier and faster. Usually, finding one specific answer in a long PDF, research paper, report, or study material takes a lot of time because the user has to search through many pages. To solve this problem, our system allows the user to upload a document and ask questions about its content. The system reads the document, breaks the information into smaller parts, and stores it in a way that makes searching easier. When the user asks a question, the system looks for the most relevant information from the document and uses an AI model to generate a suitable answer. This approach helps the system give answers based on the uploaded document instead of giving unrelated information. The system can reduce manual effort and save time while working with large documents. It can be useful for students, teachers, researchers, and organizations that regularly work with documents. Overall, the proposed system provides a simple and user-friendly way to search documents and get answers. In the future, the system can be improved by supporting more file formats, multiple languages, voice-based questions, and better answer accuracy.