Machine learning interatomic potentials have emerged as a powerful tool for bypassing the spatio-temporal limitations of ab initio simulations, but major challenges remain in their efficient parameterization. We present AL4GAP, an ensemble active learning software workflow for generating multi-composition Gaussian approximation potentials (GAP) for arbitrary molten salt mixtures. The workflow capabilities includes: (1) setting up user-defined combinatorial chemical spaces of charge neutral mixtures of alkaline/alkaline earth metals/heavy elements (e.g. Neodymium, Thorium) with halide ions, (2) configurational sampling using low-cost empirical parameterizations, (3) active learning for down-selecting configurational samples for single point density functional theory calculations at the level of SCAN exchange-correlation functional, and (4) Bayesian optimization for hyperparameter tuning of two-body and many-body GAP models. We apply the AL4GAP workflow to showcase high throughput generation of five independent GAP models for multi-composition binary-mixture melts, each of increasing complexity with respect to charge valency and electronic structure, namely: LiCl-KCl, NaCl-CaCl 2 , KCl-NdCl 3 , CaCl 2 -NdCl 3 and KCl-ThCl 4 . Our results indicate that GAP models, can accurately predict structure for diverse molten salt mixture with DFT-SCAN accuracy, capturing the intermediate range ordering characteristic of the multi-valent cationic melts.