ML to detect Cryptosporidium and Giardia are robust and precise. For instance, deep-learningbased image classification models such as ParasNet and MCellNet are accurate in detecting these two parasites in drinking water. They have the power of ML in classifying parasites from the cell-level scattering images. In addition, a linear ML model can predict the contamination of these two parasites in surface water and drinking water , offering a valuable tool to control waterborne diseases. An alternative statistical model uses linear discriminant function analysis (LDFA) to predict the appearance of Cryptosporidium and Giardia in drinking water . That model uses microbiological, physicochemical, and meteorological parameters to classify the contamination of Cryptosporidium and Giardia into four categories: none, low, moderate, and high (oo)cysts concentrations. LDFA has accuracies of 75% and 69% in predicting Cryptosporidium and Giardia, respectively.