Uncertainty is an inherent challenge in Natural Language Processing (NLP), affecting various stages of model development and application, from data collection to prediction and interpretation. This chapter explores the concept of uncertainty within the context of NLP, examining its impact on language models, their training, and their real-world deployment. It addresses the different types of uncertainty—epistemic and aleatoric—and their manifestation in NLP tasks such as text classification, named entity recognition, and machine translation. The chapter also highlights techniques for quantifying and mitigating uncertainty, including Bayesian methods, Calibration methods, and Conformal prediction. Furthermore, we discuss the implications of uncertainty for model interpretability, robustness, and fairness, offering insights into how incorporating uncertainty can lead to more reliable and transparent NLP systems. Through case studies and practical examples, this chapter provides a comprehensive understanding of how uncertainty affects NLP and outlines strategies for managing it in real-world applications.