A big challenge of sustainable power systems is to integrate climate variability into the operational and long term planning processes. In this paper, we focus on the run-of-river based hydro power generation. In particular , we deal with the modeling of this form of power production based on weather variables. Translating time series of meteorological data (precipi-tations, snowfall and air temperature) into time series of run-of-river based hydro power generation is not an easy task as it is necessary to capture the complex relationship between the availability of water and the generation of electricity. Indeed, this kind of hydro power generation is limited by the flow of the river in which the power plants are located. Moreover, the water flow is a nonlinear function of the weather variables and the physical characteristics of the river basins. Finally, the impact of the weather variables on the runoff may occur with a certain delay, whose determination depends on physically based phenomena (e.g., melting snow-local temperature). This work aims at formalizing an efficient technique for the prediction of the run-of-river based hydro power generation. Several well-established regression algorithms based on machine learning are used and compared in terms of correlation coefficient, adjusted coefficient of determination, mean absolute and mean square percentage errors. We consider three case studies: France, Portugal and Spain. Results indicate that the models based on ensemble of trees and neural networks exhibit the best performance for evaluating both the short term and the long term hydro power generation.