Clathrate hydrates enable the storage of hydrocarbons in solid form with considerably lower energy requirements than conventional methods. In this study, we use Machine Learning (ML) techniques to develop and validate a model that describes clathrate formation, drawing on laboratory data from propane, ethane, methane, and carbon dioxide. The ML-based framework augments traditional analytical and theoretical approaches by predicting equilibrium points of gas compounds and guiding the process parameters to meet scientific and engineering needs. In addition, we address critical challenges in framing the problem for ML modeling, including selecting pertinent input and output variables and incorporating ML outputs into the physical-chemical equations, thereby providing a physics-informed estimate of the stored gas volume.