The impacts and benefits of thermal insulations on saving operational energy have been widely investigated and well-documented. Recently, many studies have shifted their focus to comparing the environmental impacts and CO₂ emission-related policies of these materials, which are mostly the Embodied Energy (EE) and Global Warming Potential (GWP). In this paper, machine learning techniques were used to analyse the untapped aspect of these environmental impacts. A collection of over 120 datasets from reliable open-source databases including Okobaudat and Ecoinvent, as well as from the scientific literature containing data from the Environmental Product Declarations (EPD), was compiled and analysed. Comparisons of Multiple Linear Regression (MLR), Support Vector Regression (SVR), Least Absolute Shrinkage and Selection Operator (LASSO) regression, and Extreme Gradient Boosting (XGBoost) regression methods were completed for the prediction task. The experimental results revealed that MLR, SVR, and LASSO methods outperformed the XGBoost method according to both the K-Fold and Monte-Carlo cross-validation techniques. MLR, SVR, and LASSO achieved 0.85/0.73, 0.82/0.72, and 0.85/0.71 scores according to the R² measure for the Monte-Carlo/K-Fold cross-validations, respectively, and the XGBoost overfitted the training set, showing it to be less reliable for this task. Overall, the results of this task will contribute to the selection of effective yet low-energy-intensive thermal insulation, thus mitigating environmental impacts.