Computational methods based on machine learning have had extensive development and application in hydrology, especially for modelling systems that do not have enough data. Within this problem, there are data series that are missing, and that should not necessarily be discarded; this is achieved by means of the imputation of the same ones, obtaining complete sets. For this reason, this research proposes a comparison of computer-learning techniques to identify those best suited for hydrographic systems of the Pacific of Ecuador. For the elaboration of this investigation, the hydro-meteorological records of the monitoring stations located in the watersheds of the Esmeraldas, Caar and Jubones Rivers were used for 22 years, between 1990 and 2012. The variables that were imputed were precipitation and flow. Automatic learning machines of the Python Scikit_Learn module were used; these modules integrate a wide range of automated learning algorithms, such as Linear Regression and Random Forest. Finally, results were obtained that led to a minimum useful mean square error for Random Forest as an automatic machine-learning imputation method that best fits the systems and data analyzed. ; Computational methods based on machine learning have had extensive development and application in hydrology, especially for modelling systems that do not have enough data. Within this problem, there are data series that are missing, and that should not necessarily be discarded; this is achieved by means of the imputation of the same ones, obtaining complete sets. For this reason, this research proposes a comparison of computer-learning techniques to identify those best suited for hydrographic systems of the Pacific of Ecuador. For the elaboration of this investigation, the hydro-meteorological records of the monitoring stations located in the watersheds of the Esmeraldas, Caar and Jubones Rivers were used for 22 years, between 1990 and 2012. The variables that were imputed were precipitation and flow. Automatic learning machines of the Python Scikit_Learn module were used; these modules integrate a wide range of automated learning algorithms, such as Linear Regression and Random Forest. Finally, results were obtained that led to a minimum useful mean square error for Random Forest as an automatic machine-learning imputation method that best fits the systems and data analyzed. ; Mtodos computacionais baseados em aprendizado de mquina tiveram amplo desenvolvimento e aplicao em hidrologia, especialmente para modelagem de sistemas que no possuem dados suficientes. Dentro deste problema faltam sries de dados que no devem ser necessariamente descartadas. Isso feito por meio da imputao das mesmas obtendo-se conjuntos completos. Por este motivo, esta pesquisa prope uma comparao de tcnicas de aprendizagem computacional para identificar aquelas mais adequadas aos sistemas hidrogrficos do Pacfico do Equador pelo interesse representado pelo estudo destes sistemas por complementaridade hidrolgica. Para a elaborao desta investigao foram utilizados os registros hidrometeorolgicos das estaes de monitoramento localizadas nas bacias dos rios Esmeraldas, Caar e Jubones durante 22 anos, compreendidos entre 1990 e 2012. As variveis imputadas foram precipitao e vazo. Foram utilizadas mquinas de aprendizagem automtica do mdulo Python Scikit_Learn; esses mdulos integram uma ampla gama de algoritmos de aprendizagem automatizados, como Linear Regression e Random Forest. Finalmente, foram obtidos resultados que levaram a um erro quadrtico mdio til mnimo para Random Forest como um mtodo de imputao de aprendizado de mquina automtico que melhor se ajusta aos sistemas e dados analisados.