The present study provides a comprehensive perspective on the unique opportunities that present day artificial intelligence based meta-modelling strategies pose in internal combustion engine system identification paradigms, especially in its multi-objective calibration domains. The pertinence of the advantages of such AI based meta-modelling strategies and the potential of swarm optimization strategies have been rationalized with the chronological growth in the contemporary necessities of the diesel engine. The study clearly establishes the pivotal significance of perusing true multi-objective optimization strategies commensurate with the increasing need to address the emission-performance trade-off challenges of the diesel engines of the day. In order to highlight the credibility and scalability of such meta-model based multi-objective optimization opportunities in internal combustion engine domains, the present study presents a unique case study showing the distinct possibility of harnessing the synergistic potential of a computationally cost effective and commendably accurate meta-model based calibration endeavour in an existing diesel engine. The study was first-of-a-kind foray into the complexities of a hydrogen-diesel dual fuel operation under EGR application of varying thermal signatures along with the introduction of a trade-off merit index in the optimization workflow. The architecture was based on an ANN system identification platform wherein the MOPSO algorithm was employed to improve the emission-performance trade-off characteristics of the dual fuel operation. Further, in order to corroborate the contemporary relevance of the multi-objective optimization endeavour, additional constraints of the EPA Tier 4 diesel emission mandates were imposed to the case study. Validation of the optimization results indicated a 10.2%, 30.6%, 25.4% and 9.4% improvement in the performance-emission trade-off footprint when compared with the corresponding experimental dual fuel-EGR operations at 20%, 40%, 60% and 80% full load steps respectively. (C) 2017 Elsevier Ltd. All rights reserved.