Non-domestic buildings contribute 20% of the UK’s annual carbon emissions. A contribution exacerbated by its ageing stock of which only 7%is considered new-build. Consequently, the government has set regulations to decrease the amount of energy take-up by buildings which currently favour deep energy retroﬁtting analysis for decision-making and demonstrating compliance. Due to the size and complexity of non-domestic buildings, identifying optimal retroﬁt packages can be very challenging. The need for eﬀective decision-making has led to the wide adoption of artiﬁcial intelligence in the retroﬁt strategy design process. However, the vast retroﬁt solution space and high time-complexity of energy simulations inhibit artiﬁcial intelligence’s application. This paper presents an energy performance prediction model for non-domestic buildings supported by machine learning. The aim of the model is to provide a rapid energy performance estimation engine for assisting multi-objective optimisation of non-domestic buildings energy retroﬁt planning. The study lays out the process of model development from the investigation of requirements and feature extraction to the application on a case study. It employs sensitivity analysis methods to evaluate the eﬀectiveness of the feature set in covering retroﬁt technologies. The machine learning model which is optimised using advanced evolutionary algorithms provide a robust and reliable tool for building analysts enabling them to meaningfully explore the expanding solution space. The model is evaluated by assessing three thousand retroﬁt variations of a case study building, achieving a root mean square error of 1.02 kgCO2/m2 × year equal to 1.7% of error.