The present work explores the potential of an artificial neural network platform to emulate the performance, emissions and stability indices of an existing single cylinder diesel engine operating in dual-fuel mode with methanol port injection under varying fuel injection pressure. This investigation is further augmented by hydrous methanol injection strategies. Brake power, fuel injection pressure, diesel specific fuel consumption, methanol specific fuel consumption, air flow rate, exhaust oxygen and temperature have been chosen as the model inputs while oxides of nitrogen, unburned hydrocarbon, carbon monoxide, carbon dioxide, soot have been chosen as the emission responses to be modelled along with equivalent brake specific fuel consumption as the performance response and coefficient of variance of indicated mean effective pressure as the stability parameter to be estimated. Absolute, relative and percentage-based statistical error metrics have been employed for model evaluation. The developed model shows an excellent agreement with the experimental data as evident from its extremely low normalized mean square error, symmetric mean absolute percentage error, Normalized root mean square error, mean squared relative error footprint coupled with high coefficient of determination which was observed to be within a range of 0.983-0.9999 and a corresponding Nash Sutcliffe coefficient of efficiency of 85%-99.6%. Furthermore, low Theil uncertainty evaluation and Kullback-Leibler Divergence values imparted a commendable credence of robustness to the estimation capability of the developed model. The present study manifests a computationally efficient and reliable virtual sensing platform to simultaneously emulate the emission-performance and stability parameters of a diesel-methanol partially premixed dual fuel operational paradigms in real time engine control strategies.