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Chlorination is one of the most important stages in the treatment of drinking water due to its effectiveness in the inactivation of pathogenic organisms. However, the reaction between chlorine and natural organic matter (NOM) generates harmful disinfection by-products (DBPs), such as trihalomethanes (THMs). In this research, drinking water quality data was collected from the distribution networks of 19 rural and semi-urban systems that use water sources as springs, surfaces, and a mixture of both, in three provinces of Costa Rica from April 2018 to September 2019. Twelve models were developed from four data sets: all water sources, spring, surface, and a mixture of spring and surface waters. Linear, logarithmic, and exponential multivariate regression models were developed for each data set to predict the concentration of total trihalomethanes (TTHMs) in the distribution networks. Concentrations of TTHMs were found between < 0.20 and 91.31 µg/L, with chloroform being the dominant species accounting for 62% of TTHMs on average. Turbidity, free residual chlorine, total organic carbon (TOC), dissolved organic carbon (DOC), and ultraviolet absorbance at 254 nm (UV254) showed a significant correlation with TTHMs. In all the data sets the linear models presented the best goodness-of-fit and were moderately robust. Four models, the best of each data set, were validated with data from the same systems, and, according to the criteria of R2, standard error (SE), mean square error (MSE), and mean absolute error (MAE), spring water and mixed spring/surface water models showed a satisfactory level of explanation of the variability of the data. Moreover, the models seem to better predict TTHM concentrations below 30 µg/L. These models were satisfactory and could be useful for decision-making in drinking water supply systems. [ABSTRACT FROM AUTHOR]