This project develops a novel Telegram chatbot system capable of interpreting text, summarizing chat dialogues, and providing contextually relevant responses. The Groq API, natural language processing (NLP), and machine learning (ML) models are utilised in the solution to enhance the engagement and interest of group discussions. The chatbot use sophisticated summarisation algorithms and semantic modelling to aggregate extensive knowledge from lectures and present it clearly. It also proposes comments that maintain the discourse and relevance, so sustaining interest and preventing information overload. Our system distinguishes itself from conventional chatbots by generating responses and summarising themes. This facilitates a more fluid dialogue. The chatbot receives an average rating of 4.5 out of 5 for both accuracy and relevancy, according to user evaluations. It additionally responds to enquiries in under two seconds. The study comprises several components, including semantic interpretation, data preparation, and answer production, all employing Python and the Telegram Bot API. The document offers an extensive examination of the system’s architecture, functionality, and prospective applications in several industries, including industry, customer service, education, and collaboration. This work qualifies the system as a flexible instrument for efficiently directing digital communication since it also exhibits its scalability and flexibility to various conversational situations.