Network traffic congestion is the major challenge in telecom service providers where usually they use a limited resource to deliver the services to their customers. That leads to network performance and Quality of Services (QoS) degradation so that do not meet customer satisfaction. The Very Small Aperture Terminal (VSAT) network in Ethio Telecom delivers broadband services through satellite with a limited capacity. Therefore, this thesis aims to study the VSAT network traffic patterns to propose the traffic forecasting model. That will be used as a solution to enhance the network resources based on the prediction of the future traffic demand and as input for network planning and optimization works. In this study, the VSAT data traffic recorded for one year from 01-Mar-2020 to 28-Feb-2021 was collected. The dataset is used for data preprocessing, statistical analysis, model training and testing. All the tasks are performed by Python software. In addition, existing time series forecasting methods are selected from statistical and machine learning models that includes the Exponential Smoothing Methods (ESM), Autoregressive Integrated Moving Averages (ARIMA), Seasonal ARIMA (SARIMA), Artificial Neural Network (ANN) variants Multilayer Perceptron (MLP), Recurrent Neural Network (RNN) and Long Short Term Memory (LSTM). The forecasting accuracy metric of Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) are used to evaluate the forecasting performance of the models. These are applied to examine and choose the model having the minimum forecasting errors. As a result, the RNN model is identified the best model and improved the forecasting performance by 44.94% than the Triple Exponential Smoothing (TES) model which is a variant of ESM. Therefore, the RNN model is proposed to Ethio Telecom for future network planning and optimization to VSAT networks.