Daylight Glare Probability (DGP), founded on the latest glare metric, is the main challenge related to daylight glare inside buildings. Studies showed that the DGP depends on several factors, such as vertical illuminance values at the human eye factor, which is an essential parameter. In this study, we implement machine learning techniques to estimate and predict the DGP classifications, which are imperceptible, perceptible, disturbing, and intolerable based on the various influenced factors. A series of machine learning simulations have been conducted to investigate how those factors can be influenced by the degree of glare and classifications. In this research, different machine learning algorithms such as Artificial Neural Networks (multi-layer perceptron), K-Nearest Neighbors (KNN), Support Vector Machines (SVM), and Random Forest (RF) were employed to determine or predict the DGP classifications accurately. Results showed that the RF is the most effective method to classify the DGP and can predict with up to 99 % accuracy. Furthermore, the results displayed that vertical illuminance at eye level (lux), Ev, compared with other factors, has the largest influence on the DGP classifications. Consequently, machine learning is a powerful, promising, and viable option to implement in building constructions to optimize energy consumption, a global issue in the current century.