Graph Neural Networks (GNNs) are causing a revolution in Natural Language Processing (NLP) by capturing the complex relationships in text data. Regular sequence models often have trouble with the intricacies of language, which can hold back tasks like sentiment analysis and multi-hop question answering. GNNs allow sentences to transform into graph representations showing the links between words and phrases, which improves contextual understanding. This paper looks at how GNNs help to model complex linguistic interactions boosting various NLP tasks through methods like message passing and edge weighting. By viewing NLP tasks as graph-based problems, GNNs not improve representation learning but also boost performance in uses such as entity recognition and relation extraction. What’s more, GNNs help to make NLP models easier to understand addressing the crucial need for clarity in sensitive areas like healthcare and finance. This blending of GNNs into NLP highlights their potential to enhance both the effectiveness and reliability of language processing systems.