Energy efficiency and occupant wellbeing are complex concepts increasingly becoming a mainstream building and construction industry focal point. These concepts demand deciding not only the appropriate building materials, techniques, and systems but also abstract qualities, which are challenging to quantify. As recent automation technologies have advanced, the building and construction sector is experiencing rapid progress, bringing about efficient building development methods. However, building design needs an efficient computerized design tool that enables designers to make more reliable decisions to help achieve the intended quality objectives of the buildings. This paper aims to explore the data preparation of energy-efficient and healthy buildings to be utilized in a machine learning (ML) model that can accurately predict the determination of the building variables. The generalized data used in this study were quantified, analyzed, and processed before being utilized in the machine learning model developed using Support Vector Regression (SVR) and Multi Layer Perceptron (MLP) algorithms. The accuracy of the models was evaluated using the Mean Absolute Error (MAE). The outcome of this study shows that the predictive machine learning model could help decision-makers quantitatively predict the healthy building variables to an adequate level of accuracy.