Precipitation predictions in urban areas are considered very relevant in the context of civil engineering, the economic and architecture, becoming a determinant of urban planning. This study aims to modeling and predicting nivel rainfall in the urban area of the city of Santo Domingo de Los Tsachilas in Ecuador with artificial intelligence. In this research, statistical techniques of simple linear regression and time series are used using the integrated autoregressive model of moving average with the Rstudio package, for the climatic factor of precipitation from meteorological stations the La Concordia and Puerto Ila, from the years 2015 to 2022. The results show that rainfall presents a non-significant decreasing trend for the two stations according to the Pearson tests and the Kendall Mann tests. The precipitation simulations for the La Concordia station showed an ARIMA (0,1,1) (2,1,0)[12] and the Puerto Ila station recorded an ARIMA (2,1,0) (2,1,0)[12] simulations obtained through 96 iterations provided by artificial intelligence. It is concluded in this investigation that the precipitation variable presents a decreasing trend and with a high variability of the observed and forecast data in the station closest to the urban area, a factor causing the climate impact.