<p>Ionogels are emerging as promising electrolytes for use in electrochemical and energy storage devices. Their practical implementation, however, critically depends on thermomechanical stability, which is strongly governed by the glass transition temperature (<i>T</i>_g). Predictive tools for estimating the <i>T</i>_g of ionogels remain scarce despite the growing interest in ionogels. This lack of predictive capability limits the rational design and screening of ionogel materials for targeted applications. In this study, ionogels composed of silane and titanate combined with imidazolium-based ionic liquids are investigated to develop a methodology for estimating the <i>T</i>_g. The approach applied here is the Group Contribution Method (GCM), in which a genetic algorithm has been employed to determine the contribution parameters of each structural group, and non-linear regression has been used to construct predictive <i>T</i>_g models. The model is built using 26 experimental <i>T</i>_g data points, achieving an overall absolute average deviation (AAD) of 1.88%. When extended to estimate the <i>T</i>_g of two additional ionogels, the method yielded an AAD of 3.5%. While both Quantitative Structure–Property Relationships (QSPR) and GCM have previously been successfully applied to predict various thermophysical properties of ionic liquids—the primary precursors of ionogels—this study represents the first reported attempt to estimate the <i>T</i>_g of ionogels using the GCM approach.</p>