In the digital age, video content on platforms like YouTube has become an integral source of information. However, the sheer volume and length of video transcripts pose challenges for efficient information retrieval. This project introduces a solution, the YouTube Video Transcript Summarizer, aimed at streamlining the extraction of valuable insights from videos. Leveraging natural language processing (NLP) techniques, Speech-to-Text capabilities, and Large Language Models (LLM), our system condenses lengthy video transcripts into concise and informative summaries. Furthermore, it incorporates a multi-lingual question-answering bot functionality to enhance the accessibility of specific content. The scope of this paper encompasses demonstrating the potential benefits of such a system to a diverse range of users, from content creators to researchers and language learners. By making video content more accessible and manageable, the YouTube Video Transcript Summarizer addresses the growing need for efficient information retrieval from multimedia sources in the digital era.