Mobile network traffic prediction is an important input into network capacity planning and optimization. Existing approaches may lack the speed and computational complexity to account for bursting, non-linear patterns, or other important correlations in time series mobile network data. We compare the performance of two deep learning (DL) architectures, long short-term memory (LSTM) and gated recurrent unit (GRU), and two conventional machine learning (ML) architectures—Random Forest and Decision Tree—for predicting mobile Internet traffic using 2 months of Telecom Italia data for Milan. K-Means clustering was used a priori to group cells based on Internet activity, and the Grid Search method was used to identify the best configurations for each model. The predictive quality of the models was evaluated using root mean squared error and mean absolute error. Both DL algorithms were effective in modeling Internet activity and seasonality, both within days and across 2 months. We find variations in performance across clusters within the city. Overall, the DL models outperformed the conventional ML models, and the LSTM outperformed the GRU in our experiments.