Self-compacting concrete (SCC) is an innovative building material developed to have special properties such as increased flowability, good segregation resistance and compaction without vibration etc. Despite the benefits of SCC over conventional concrete, there are very few methods reported in the literature that can predict the SCC compressive strength accurately. Thus, to foster the utilization of SCC in construction industry, an inventive machine learning technique named Multi Expression Programming (MEP) is employed to forecast the SCC 28-day compressive strength. A database consisting of 231 compressive strength results is constructed using extensive literature search. The resulting equation obtained by employing MEP algorithm relates the compressive strength of SCC with six most influential input parameters i.e., water-cement ratio, amount of fly ash and silica fume, quantities of fine and coarse aggregate and superplasticizer dosage. The database is split into training and validation datasets used for training and validation of the algorithm respectively. The accuracy of MEP algorithm is verified by means of four statistical error metrices: mean absolute error (MAE), root mean square error (RMSE), coefficient of correlation (R) and coefficient of determination ( ). The results revealed that the errors are within the prescribed limits for both training and validation sets and the developed equation have excellent generalization capacity. This is also verified from the scatter and series plots of the training and validation datasets. Thus, the developed equation can be used practically to forecast the strength of SCC containing fly ash and silica fume.