The evaluation of the quality of the water in rivers is necessary to manage the efficiency of its use, being necessary to carry out physicochemical and biological analyzes to determine its healthiness, but it implies in its determination of a series of parameters that use various analytical methods that often they are tedious and time consuming to calculate. The present study makes a comparison of machine learning models such as Multiple Linear Regression (MLR), Neural Network Backpropagation (BPNN) and Support Vector Regression (SVR) to estimate Dissolved Oxygen (DO) and Biochemical Oxygen Demand (BOD) to determine the quality of the water of the Rímac river. Water samples were collected from 26 stations and non-point sources of contamination along the Rímac River with 624 records made during the years 2010 to 2012. The physical and chemical parameters introduced in the models include...