Gas hydrate inhibitors are currently injected at the upstream of pipelines based on the calculated/measured hydrate stability zone, worst case scenarios for pressure and temperature conditions, water cut, and the inhibitor loss to the nonaqueous phases. In general, no means of controlling and monitoring are available along the pipeline and/or downstream to assess the degree of hydrate inhibition. In many cases, high safety margins are used to account for the uncertainties in the above factors and minimize the gas hydrate formation risks. In this work, the possibility of predicting the hydrate safety margin from refractive index data of aqueous solutions is investigated using an artificial neural network method, which ensures high flexibility of the functional form for the regression. The developed method considers the changes in index of refraction with respect to refractive index of pure water for a given aqueous solution. Independent data are used to examine the reliability of this tool. The predictions of this method are in acceptable agreement with the independent experimental data, demonstrating the reliability of the artificial neural network method for estimating the hydrate safety margin in the presence of salt or organic inhibitor using refractive index data of aqueous solutions.