In pipelines and process equipment, especially in cold oceanic environments, gas hydrate development presents a serious problem to the petroleum industry. Getting around this problem efficiently requires an understanding of the chemical thermodynamics of gas hydrate formation. In order to forecast the temperature of gas hydrate formation, the current investigation compares the effectiveness of three different types of machine learning algorithms: Support Vector Regression (SVR), Artificial Neural Networks (ANNs), and Decision Tree Regression (DT). The research was conducted using Python 3.11.3 as the programming framework, which made use of its extensive ecosystem of open-source tools, including scikit-learn (version 1.2.2) and Keras with TensorFlow. With ANNs, there was no activation function in the output layer and the hyperbolic tangent function was used as the activation function in a hidden layer. The Radial Basis Function (rbf) was used as the Kernel function for Support Vector Regression (SVR). A maximum tree depth of 15 was imposed on the Decision Tree (DT) regression. Throughout the whole dataset, evaluation measures such as Root Mean Square Error (RMSE) and coefficient of determination (R2) were calculated. The findings showed that the R2/RMSE values for SVR, ANNs, and DT regression were, respectively, (0.9999, 0.0631), (0.9986, 0.5011), and (0.9278, 3.5606). In conclusion, the models' output was rated as follows in descending order: Support vector regression (SVR) is a subset of decision tree regression (DT) and artificial neural networks (ANNs). Following that, a Web User Interface (WUI) was created using the Decision Tree paradigm, which proved to be the most efficient. In theoretical terms, this work opens the door to further developments in gas engineering. The prediction capability of the models could potentially further improved by adding more experimental data to the dataset used for training.