This research presents a novel system for dynamic content generation and interactive game creation, leveraging natural language processing (NLP) and game development techniques. The system integrates OpenAI’s GPT-3.5 API for generating text-based responses and multiple-choice questions (MCQs) based on user-uploaded documents (PDF or DOCX files). The extracted content is handled and analyzed, giving important literary yields and instructive amusement encounters. The game is planned utilizing PyGame, where clients reply questions created from the record content, displayed in a locks in visual arrange. By utilizing Streamlit as the primary interface, the framework empowers clients to associated consistently with record transfer, substance era, and gameplay components. The MCQ game powerfully adjusts based on the substance given, making the stage profoundly customizable for different instructive, proficient, and inquire about applications. This framework illustrates how record handling, NLP, and intelligently gameplay can be harmonized to make a natural and intelligently involvement. The methodology created in this investigate can serve as a system for future applications in instructive innovations, content investigation instruments, and intuitively learning stages. Comes about appear the systems viability in substance extraction and reaction era, giving a smooth client involvement whereas showcasing the potential of AI in intelligently learning environments.