The corrosion of zirconium alloy cladding significantly affects its mechanical and thermal properties, making it a critical factor that must be considered in nuclear fuel design. However, most existing corrosion models are based on semi-empirical methods, which do not offer ideal fitting accuracy. Although machine learning techniques can construct models with higher precision, these models often lack the ability to quantify uncertainty, thereby limiting their reliability. This study introduces and compares two methods for assessing model uncertainty in the field of machine learning and applies them to both semi-empirical and machine learning models. The first method divides model uncertainty into epistemic uncertainty and aleatory uncertainty, modeling each through ensemble models and the negative log-likelihood function, respectively. The second method employs conformal prediction techniques, providing the model with prediction intervals that are confidence-guaranteed without presupposing any distribution assumptions. The results from the test data indicate that the first method ensures all true values fall within the 2σ range, while the second method’s predictions meet the preset confidence levels. These findings offer a new avenue for the development of zirconium alloy cladding corrosion models that are both accurate and reliable.