The main objective of this study was to present a novel approach to access more accurate hydrate formation rate predicting models based on the combination of flow-loop experimental data with learning power of artificial neural networks. Therefore, more than 2,300 data of C-1, C-3, i-C-4, and CO2 hydrate formation rate in the presence of two kinetic inhibitors (PVP and L-Tyrosine) and two inhibitor intensifying additives (PPO and PEO) was used. It was found that such models can be used as powerful tools, with total errors less than 2% for the developed models, in predicting hydrate formation rate in these cases.