Abstract: Time series data often brings about the monitoring of hydrological processes. Most hydrological data are within time, connecting their analysis indirectly with the time component. When analysing time series, it is crucial to consider the fact that it consists of an internal structure (e.g., autocorrelation, trend, or seasonal variation) where data points are considered over time, therefore, forecasting hydrological data is a crucial step regarding the performance of environmental models, engineering, and research applications, and thus, it presents a significant challenge. Due to the substandard quality of precipitation data, poor results are attained then to amend it, accurate planning and management of water resources should be achieved by relying on the presence of accurately consistent precipitation data in meteorology stations. This paper aims to give a brief overview and find optimal parameters to build a Seasonal Autoregressive Integrated Moving Average (SARIMA) model using the grid search method, diagnosing time series prediction, validating the predictive rainfall, and performing rainfall forecast for the Biópio hydrological stations in the Catumbela River till 1969.