NLP and text analytics are techniques applied to read texts and extract useful insights. These technologies are widely applied in many fields to address diverse questions, utilizing advancements in current technologies, such as AI, ML, and deep learning. This research introduces an overall framework for Technical Language Processing (TLP) which integrates NLP with text analytics to enhance understanding and generalization of machine language. The framework stresses preprocessing, pattern identification, and sentence generation through structured methodologies like removal of special characters, categorizing words in length, and inputting them grammatically meaningful sentences. These would be supporting applications such as spam detection, and they'd tell the sentiment of the given passage whether positive, negative, or neutral. It does improve searching and word retrieval in the provided passage. The structured outputs and contextually relevant sentences of the framework make it possible to generate insights, automate content creation, and improve text retrieval processes. This approach ensures adaptability, scalability, and better generalization for various NLP applications.