The purpose of this paper is to create a chatbot for web scraping that leverages advanced natural language processing models and web scraping methods. The aim is to build an intelligent system that can collect information from websites and respond to users’ questions. According to the supplied search findings, here are the main aspects of how the chatbot was implemented: including the use of the Hugging Face Transformers library for question answering tasks. There are two key parts in the implementation: (1) A Python-based web scraper powered by BeautifulSoup libraries and a request module for fetching content from different online platforms. (2) A question answering pipeline that is built around transformer-based models. The QA model was trained to analyze and extract relevant responses from retrieved contexts. Web scraping systems collect content from various sources such as Wikipedia pages or specific sites, which act as contexts for generating correct replies. The chatbot uses the information it collects to answer questions asked by users, thus giving them relevant and useful responses. The main aim of this paper is to show how web scraping can be seamlessly integrated with language models based on transformers so that a chatbot can efficiently collect data from the internet and answer user inquiries appropriately. This research explains the implementation details of a web scraping chatbot system, challenges faced during development, evaluation measures used, and potential future enhancements. This paper aims to illustrate that advanced techniques in natural language processing (NLP) coupled with web scraping can lead to the creation of intelligent chatbots capable of accurately retrieving information from users’ queries.