Discourse analysis plays a significant role in understanding and interpreting the complexity of human language, providing valuable insights for improving natural language processing (NLP) systems. This study presents an in-depth review of discourse analysis in NLP, and its applications in various domains such as machine translation, question-answering systems, sentiment analysis, conversational bots, and information retrieval. By analyzing the structural and contextual aspects of language, discourse analysis enhances the NLP system’s ability to understand individual words and their meaning in cross-border conversational contexts. The review highlights how discourse analysis and learning techniques work together to improve the automated system's accuracy and efficiency. In addition to addressing significant challenges, including ambiguity, cultural variance, and conversational dynamics, the paper also suggests future research directions for exploring the possibilities of natural language processing in human-computer interactions. This work attempts to fill the gap between linguistic theory and computational applications by demonstrating how discourse analysis can revolutionize the development of more intelligent, contextually aware, and human-centered language technologies.