Model uncertainty is the academic concept and associated methodological field dedicated to identifying, quantifying, and managing the inherent limitations and potential inaccuracies of mathematical, statistical, or computational models used to represent complex systems or phenomena. This research investigates the impact of model structure, parameter choices, and simplifying assumptions on prediction reliability and decision-making, emphasizing the need to acknowledge and characterize the divergence between model outputs and true system behavior for robust scientific inference and engineering applications.