The building sector accounts for almost 40% of the total global energy consumption. Mosques are among these buildings which found to consume huge amount of energy. There is an increased demand to establish new sustainable mosques. Although the design stage proves the superiority to reduce energy consumption up to 70%, majority of previous studies were found at operation and maintenance stages. This is due to the complexity of this stage, lack of information and support tools to assist designers. The current design tools and prediction models to estimate cooling load are complex, time-consuming and fail to meet the requirement of designers especially at the design stage. This paper aims to develop a machine learning prediction model to assist mosque designers in providing a range of lowest cooling load design alternatives. In developing the model, artificial neural network was applied including back-propagation strategy and Levenberg–Marquardt algorithm. The least mean square error was at 6.27 × 10 − 9 and the accuracy of the model reached at 99.88%. Further, nine experts were participated to estimate the effectiveness and ease of use of the prediction model. The model proved to be rapid, accurate and can be used by designers with no many simulation scenarios or complex calculations.