Filling out forms is a routine but vital activity in sectors such as healthcare, finance, and business operations. Conventional approaches, like rule- based systems or browser autofill, frequently fail to adjust to changing scenarios or varied input formats. This study introduces a chatbot-based system powered by advanced machine learning and context-aware large language models (LLMs) to streamline real-time form completion across diverse fields. The approach combines natural language processing (NLP), retrieval-augmented generation (RAG), and user interaction monitoring to boost precision and flexibility. A major use case for this technology is managing large-scale events, including the FIFA World Cup 2030 and the 2025 Africa Cup of Nations, both taking place in Morocco. These events will demand efficient processing of visa requests, lodging bookings, ticket sales, and accreditation for millions of attendees, media personnel, and staff. Embedding the chatbot into government and hospitality systems can cut processing delays, reduce mistakes, and deliver a smoother user experience with real-time, multilingual, and context-sensitive support. The system’s effectiveness is tested on standard datasets and practical scenarios, showing better accuracy, speed, and user satisfaction compared to traditional autofill tools. These results emphasize how AI-powered assistants can transform digital processes by minimizing manual effort without sacrificing accuracy. Future efforts will aim to refine domain-specific adaptability and incorporate privacy-focused measures to strengthen user confidence and safeguard data.