Internet traffic can be described as a general term that includes the transmission of internet data between different devices and systems. The analysis and prediction of internet traffic is a proactive approach to ensure secure, reliable and qualitative network communication. Several linear and non-linear models are proposed and tested to analyze the prediction of network traffic, including techniques based on regression analysis, artificial intelligence and data mining. These interesting combinations of internet traffic analysis and forecasting techniques are implemented to achieve efficient and effective results. Timely and accurate prediction of the use of internet data is a topic of great importance in the telecommunications industry. In addition, internet traffic data is important for many applications in telecommunications management, such as understand customer behavior, optimal planning of the capacity of networks, successful decision making and maintaining the quality of services at guarantee level in the future. This situation inspires us to rethink data mining with internet traffic forecasting problems. This study provides an overview of data mining in telecommunications and proposes a novel model for forecasting mobile internet traffic based on artificial neural networks. The analysis of mobile Internet traffic data during the last five years shows that the month of August has a higher traffic flow, while the lowest flow was the month of June. The selected model has three layers with a determination coefficient of 97.5%.