In recent years, graphene has been widely utilised as a supercapacitor electrode, and doping heteroatom on graphene is reported to enhance the pseudocapacitance of the electrode materials significantly resulting in a high energy density. However, the relationship and charge storage mechanism of a so-called “synergistic effect” between those doped atoms including oxygen-, nitrogen-, and sulphur-doping on supercapacitor performances remain inscrutable. In this study, a machine learning model – artificial neural network (ANN) is used to predict the capacitance of heteroatom-doped graphene-based supercapacitors and establish the effects of heteroatom-doping. Trained ANN can accurately predict the capacitance of the electrode, drawing the best synthesis conditions for the heteroatom doped graphene. Furthermore, we successfully demonstrate the synergistic effect that arises from co-doping nitrogen, sulphur, and locate the optimised region for N/S-co-doping with high capacitance, and high retention rate. Machine learning methods allow us to consider a much larger space of heteroatom-doping combinations to maximise the supercapacitor performances and provide a useful guideline for co-doping graphene-based supercapacitors.