The research of the hydrate formation has advanced over the last few decades. Several models have been developed to understand the hydrates formation kinetics and conditions. In this study, computer based model is used to predict the gas hydrate formation rate. Since the hydrae formation is stochastic phenomenon and it is common to get inconsistent data, Artificial Neural Network (ANN) model has potential to outstand other conventional kinetic models. ANN used to predict the methane hydrate formation rate in multiphase system. The liquid phase composed of water + drilling oil + nonionic surfactants was used to form methane hydrates at pressure 8.80 MPa and temperature of 274.15�277.15 K. This research would essentially assess the effectiveness of ANN model for the kinetic modeling of the formation of gas hydrate from the acquired regression analysis. The result of this research revealed that ANN model with 16 number of hidden neurons had a better prediction as the highest regression value R was found to be 0.9956. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.