Analysis-driven design of Internal Combustion Engines (ICE) is extremely valuable in significantly reducing hardware investments and accelerating development of low Greenhouse Gas (GHG) emitting vehicles compliant with strict emissions regulations. Advanced physics-based engine modeling tools use system-level models coupled with Computational Fluid Dynamics (CFD) simulations to predict engine-out emissions. The success of this methodology largely relies on the accuracy of analytical predictions, especially engine-out emissions. Results show excellent agreement in prediction of engine performance parameters, oxides of Nitrogen (NOx) emissions and combustion noise, while the Carbon Monoxide (CO), Unburned Hydrocarbons (HC) and Smoke emissions predictions remain a challenge even with large chemical kinetics solvers and refined mesh resolution. In this study, a hybrid approach combining CFD analysis with Machine Learning (ML) for prediction of engine-out emissions of CO, HC and Smoke is demonstrated. Input features generated by physics-based CFD simulations and experimentally measured emissions data as labels or targets were used to train a deep Convolutional Neural Network (CNN) model. This approach led to a significant improvement in prediction accuracy of all three emissions species and captured the qualitative trends as well. The ML model could be used to augment the engine modeling toolkit leading to significantly more accurate predictions of engine-out emissions, lower computational costs and reduced turnaround times for engine simulations.