Ever growing population and progressive municipal business demands for constructing new buildings are known as the foremost contributor to greenhouse gasses. Therefore, improvement of energy eﬃciency of the building sector has become an essential target to reduce the amount of gas emission as well as fossil fuel consumption. One most eﬀective approach to reducing CO2 emission and energy consumption with regards to new buildings is to consider energy eﬃciency at a very early design stage. On the other hand, eﬃcient energy management and smart refurbishments can enhance energy performance of the existing stock. All these solutions entail accurate energy prediction for optimal decision making. In recent years, artiﬁcial intelligence (AI) in general and machine learning (ML) techniques in speciﬁc terms have been proposed for forecasting of building energy consumption and performance. This paper provides a substantial review on the four main ML approaches including artiﬁcial neural network, support vector machine, Gaussian-based regressions and clustering, which have commonly been applied in forecasting and improving building energy performance.