Telecommunication network traffic prediction is an important approach that ensure efficient network planning and management. Telecommunication network traffic is univariate and prediction models have mostly been concentrated on single-input and single-output traffic. This study proposes a new approach, the multiple-input multiple-output Coactive Neuro-Fuzzy Inference System (CANFIS) model to predict a five time span univariate hourly, daily, weekly, monthly and quarterly time series of 3G downlink traffic simultaneously. In the modelling process several parameters were used in the configuration of the network. The best model for predicting five-input telecommunication traffic was CANFIS (5-2-5) which employed a Bell membership function, Axon transfer function and Momentum learning rule and the membership function per input of 2. The performance of the model was evaluated by comparing the predicted traffic with actual traffic obtained from a 3G network operator and the results indicate a minimum accuracy measure value of MSE = 0.000486, NRMSE = 0.01120 and percent error = 12.33%.