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The present study is dedicated to the development of a Coupled Multi Objective Neuro Genetic Algorithm-based model to emulate the NOx, opacity and BSFC (diesel) of an existing single-cylinder four-stroke CI engine operated in various dual-fuel modes with hydrogen operated under EGR of varying thermal signatures. The associated MIMO problem has been transformed into an equivalent MISO model through unique coupling and decoupling algorithms. GA has been used as the search method to optimize the MISO topology. The MISO model substantially reduced the network weight in contrast to its MIMO counterpart thereby ensuring faster convergence and better accuracy with a consequently lower computational footprint and a promising potential to be used as a virtual sensing platform in real-time optimization strategies in engines. The MISO model successfully captured the thermal effects of EGR on the desired outputs.