It is very crucial to avoid hydrate formation in deep water natural gas pipeline and it has posed challenges to flow assurance. Although the ideal methods to find the hydrate formation conditions by temperature, pressure and composition of flowing gas mixtures by experimentally. But performing such methods maybe impractical as it involves the assumption of infinite number of hydrate formation prediction conditions. A novel method of artificial intelligence (AI) modelling for methane gas hydrate for subsea gas pipelines has been developed. It will help to find hydrate formation conditions for subsea pipeline. The correlations are based on the temperature with and without concentration of inhibitors during gas hydrate formation. The correlations between temperature and pressure are measured using polynomial and Fourier equations by incorporating AI optimization techniques of genetic algorithm (GA). All correlations are computed in the range of temperature 250 to 290 K and pressure 1.6 to 4.12 Mpa. The performance of developed algorithm was evaluated by comparing existing experimental data with measured correlations. © 2015 IEEE.