Liquid loading is an undesired phenomenon in gas wells that occurs when producing wells attain a flow rate below which liquid will not be able to flow to the surface. The inability of the energy from the gas to transport the liquid to the surface causes back flow and eventual accumulation of liquid at the wellbore. This is characterised by intermittent flow, which, if left unchecked, can eventually kill the well. An effective and reliable predictive method must therefore, be employed. In this study, improved models based on data set from condensate/water in a gas well were developed by applying firefly (FA) and particle swarm optimisation (PSO) algorithms. The results showed that the model developed out perform many of the existing models. The models predicted liquid loading in gas well at 86% level of accuracy compared to the 81% highest possible from published models. Although, the FA and PSO models predicted liquid loading at higher accuracy compared with Turner and Coleman models for higher wellhead pressure systems, the Coleman model appeared to perform better in the prediction of critical gas rate for low-pressure systems. However, the developed model can significantly improve the prediction of liquid loading in gas wells at a higher reliability and accuracy levels. Thus, the proposed models can be a veritable tool for accurately predicting liquid loading in gas wells.