We showcase the power of AL4GAP in enabling facile generation of GAP models at DFT-SCAN level for five different three-component molten salt mixtures. Our results indicate that GAP models can accurately capture the structural characteristics of complex mixtures of molten salts. Our workflow accelerates the development of DFT-SCAN accurate multicomposition ML-IPs, which can be used for high accuracy property prediction across various compositions and simulation conditions opening up the possibility for providing rapid feedback/guidance to experiments in regimes challenged by high corrosion and radiation.