In the field of artificial intelligence, uncertainty represents the inherent unpredictability and lack of certainty associated with predictions, data, and decision-making processes. This uncertainty can arise from various sources, including incomplete or noisy data, model limitations, and the complexity of real-world environments. Addressing uncertainty is crucial for building reliable AI systems, as it affects the accuracy of predictions and the robustness of decisions. Techniques such as probabilistic modeling, Bayesian inference, and ensemble methods are employed to quantify and manage uncertainty in AI applications. By incorporating uncertainty into models, practitioners can improve decision-making, enhance trustworthiness, and develop systems that better adapt to the dynamic nature of real-world scenarios.