Injection of gas hydrate inhibitors at the upstream of oil/gas pipelines is normally 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 many cases, high safety margins are used to account for the uncertainties in the above factors and minimize the gas hydrate formation risks as no means of controlling and monitoring are generally available along the pipelines and/or downstream to assess the degree of hydrate inhibition. In this work, the possibility of predicting the hydrate safety margin from specific gravity data of aqueous solutions is investigated using a feed-forward artificial neural network method with a modified Levenberg-Marquardt algorithm. The method, which has been developed for various salts (NaCl, KCl, CaCl2, KBr, NaBr) and organic inhibitors (methanol, ethanol, ethylene glycol, glycerol) aqueous solutions, considers the changes in specific gravity of aqueous solution for estimating the hydrate stability zone. Independent data (not used in training and developing of the neural network) are used to examine the reliability of this tool. The predictions of this method are found to be in acceptable agreement with the independent experimental data, demonstrating the reliability of the artificial neural network method for estimating the hydrate safety margin using specific gravity data of salt or organic inhibitor aqueous solutions.