The study aimed to develop and evaluate the viability of a data driven approach to predict membrane fouling in critically ill patients undergoing CRRT using machine learning algorithms. Moreover, Counterfactual Analysis is used to detect counterfactuals, i.e. the minimal modifications to the input of the machine learning model required to revert a membrane fouling prediction.The study utilizes time series from Careggi University Hospital ICU. To keep the approach simple and interpretable, the study mainly focuses on a tabular data approach, not involving the time series’ interdependence inherent within each treatment. A Shapley values based Counterfactual Analysis is applied to the best prediction model, to detect high quality counterfactuals. The specific methods adopted include Random Forest, XGBoost and LightGBM. For all these methods a rebalancing rate of 10% with respect to the majority class showed the most balance performance with a sensitivity of 0.776 and aspecificity of 0.963. The performance obtained by all methods showed to be robust with respect to different length forecasting horizons. The tabular data approach revealed not to be a limitation as it outperformed the Long-Short-Term-Memory Recurrent Neural Networks which intrinsically take into account temporal relationships. It is shown that by reducing the features to 5 via a feature selection method, we obtain more simple and interpretable models, without compromising too much the accuracy. The adopted Counterfactual Analysis method is able to detect counterfactuals which seems promising according to the considered quality score. The experimental study provides promising results concerning the adoption of data-driven machine learning methods to predict membrane fouling events during CRRT. The practical implications for clinicians and nurses managing CRRT are significant.