The integration of satellite data in rainfall-runoff simulation is of paramount importance in regions where data are limited and not easily available. The data used in this study, concerning the daily precipitation and daily runoff during the period extending from 2007 to 2015, were measured and recorded in situ. This study aims primarily to optimize the performance of the modeling carried out, using data from various satellite sources in order to complete the missing data, and consequently to predict the liquid flow in the Beni Bahdel watershed, in the northwestern region of Algeria, by applying the Long Short-Term Memory (LSTM) learning model. It is important to know that the optimization of rainfall-runoff modeling is based on the use of satellite data relating to evapotranspiration, mean temperatures, minimum and maximum temperatures, net radiation, wind speed, and relative humidity. These data come from the NOAA CPC, ERA, ERA5_AG, ERA5-Land, GLDAS, CFSR and MERRA2 satellites. In addition, two statistical indicators were calculated to perform this optimization that is based on the LSTM approach that integrates remote sensing data, the coefficient of determination (R²), and the Nash–Sutcliffe Efficiency (NSE). Thus, a performance difference of about 0.30 was observed, for the NSE and R² coefficients, between the CFSR temperature data (NSE of 0.61and R² of 0.61) and the maximum and minimum temperature data from the ERA5-LAND and ERA5 satellite sources (0.92 for NSE and 0.93 for R²), for the validation period. This significant difference suggests that the use of the minimum and maximum temperature data from the ERA5-LAND source allows achieving a rainfall-runoff modeling wih optimal performance. Indeed, the findings showed that quite high performances were achieved for the calibration period (0.93 for NSE and 0.95 for R²) and for the validation period (0.92 for NSE and 0.93 for R²).