Intravenous fluid therapy is one of the most common in- terventions for hospitalised patients admitted to intensive care. In particular, patients may be subject to a progres- sive and dangerous accumulation of fluids. In this con- text, we can define Fluid Creep (FC) as those fluids used to dilute drugs and nutritions and to maintain catheter pa- tency. This single-center, retrospective study was carried out on the MargheritaTre database and included 4786 pa- tients with an average of 1606 ml (1 quartile 849-3 quar- tile 2000) of FC in the first 24 hours of intensive care unit admission. The objective of this analysis is to identify vari- ables significantly associated with FC, initially by means of a linear model and subsequently by means of a classifi- cation model aimed at identifying patients at risk of receiv- ing high FC using explainable artificial intelligence (AI) techniques. After comparing the performance of seven ma- chine learning models, logistic regression was found to be the model with the best accuracy on the test set of 0.76. Therefore, the SHAP (SHapley Additive exPlanations) al- gorithm was applied to conduct an explainable AI analy- sis, with the aim of interpreting the behaviour of the model and determining the most relevant variables in classifying the risk of high FC.