These days, we’re attempting to use robots to automate a lot of our regular tasks. These systems reduce human effort and facilitate our work; they are especially useful for those of us who have certain physical impairments. We repeatedly came to the conclusion that those without arms or with hearing problems find writing in class to be quite difficult. They encounter numerous difficulties as a result, such as the inability to write in tests or prepare for classes without assistance, while occasionally people without disabilities also experience writing difficulties due to time constraints or other circumstances. The purpose of this study is to provide a tool that will facilitate writing for us and organized genrated data. The proposed model will reduce the necessity for stenographers because they are more prone to error. The model focuses on accurately transcribing speech into text and write it down on the paper.The combination of an NLP-based writing machine and the K-means clustering algorithm can be utilized to enhance the capabilities of text generation and organization. These machines have found applications in multiple domains, including content creation, customer support, translation, and creative writing. In content creation, NLP-based writing machines can automate the generation of articles, blog posts, and product descriptions, saving time and effort for content creators. They can also provide personalized responses and support in customer service interactions, enhancing user experiences and improving efficiency. Moreover, these machines are instrumental in translation tasks, facilitating cross-lingual communication by swiftly translating text between languages.As the field of NLP continues to advance, future research could focus on improving the models' abilities to understand and generate context-specific content, enhance multi-modal capabilities by integrating text with other forms of media, and address the challenges of bias and fairness in automated text generation.By combining NLP-based text generation with K-means clustering, it is possible to organize and generate text in a more structured and contextually meaningful manner. This integration can facilitate applications such as content generation for specific domains, personalized text generation, or organizing large text corpora into coherent clusters for analysis and exploration