Water quality is being altered by human activities and monitoring them is highly recommended. Nevertheless, low-cost monitoring nodes are generally limited in terms of computation capacity. In this paper, we analyse the performance of different machine learning algorithms trained and tested in nodes as part of edge computing in a water quality monitoring network. The performance of five well-known algorithms for data classification in multiclass and binary classification is assessed. The algorithms are coded to run in a low-cost node, and their performance is compared with Matlab results. Accuracy and precision are the metrics used for the comparison, with the artificial neural network as the algorithm with higher performance, followed by the Naive Bayesian classifier. The accuracy for the multiclass classification in the node is slightly better, 98.66%, than the one offered by the predefined algorithm in Matlab, 91.61%, and the precision was the same. The performance with the binary classification was 100% in all the cases and metrics. Focusing on the classification time in the node, Naïve Bayesian classification required less time (0.6 and 1.3 s) than artificial neural network (1.6 and 10.3 s) for binary and multiclass classification. Thus, we can confirm that these algorithms can be used as part of edge computing in low-cost nodes without greatly impacting the required time for training the models.