This study focuses on different intelligent time series modelling techniques namely nonlinear autoregressive network with exogenous inputs (NARX), autoregressive integrated moving average with external inputs (ARIMAX), multiple linear regression (MLR), and regression error with autoregressive moving average (RegARMA), applied on a diesel engine to predict NOx emission and fuel consumption. The experiment data are collected from a six cylinder, four stroke medium duty truck diesel engine, which is integrated on a passenger bus and operated in engine integration tests. NOx emission and fuel consumption outputs are estimated with the help of input data; exhaust gas recirculation temperature and position, engine coolant temperature, engine speed, exhaust gas pressure, common rail pressure, intake manifold air temperature and pressure, accelerator pedal percentage, engine load, turbocharger variable geometry position and speed, and selective catalytic reduction outlet temperature. NARX artificial time series neural network, MLR, ARIMAX, and RegARMA time series techniques were separately applied for the estimation NOx emission and fuel consumption outputs. The performance of the models is analyzed and evaluated with Bayesian information criterion (BIC) and root mean square error (RMSE) criteria. When the high cost and time loss of experimental testing are thought, using the intelligent modelling methodology provides far more accurate prediction and fast application abilities to analyze internal combustion engine dynamics for the control and calibration manner. As a result of the comparison of different types of modelling techniques, RegARMA technique comes to the forefront with 6707.6 BIC value with 105.58 RMSE for NOx emission model and 4026.4 BIC value with 7.93 RMSE for fuel consumption model.