Designing the chemical composition of multi-component glasses toward a set of target properties requires information on a complex range of trade-off correlations. Consistent reference datasets are typically not available for this size, which will allow for the training of multi-task neural network models. As an alternative, multi-output Gaussian process regression (mGPR) is employed for multi-task machine learning (ML) of combinations of glass properties from small, but highly consistent datasets, such as those typically generated in coherent laboratory campaigns. To this end, a dataset on polyionic glasses from the system of Na2O-AlF3-Al2O3-P2O5-SO3 is used, in which property resilience to variations in chemical composition is a particular challenge given the presence of multiple volatile species. Using the example of simultaneous predictions of refractive index and Young’s modulus, it is demonstrated how mGPR outperforms single-task modeling even when one of the physical properties is used as an additional output feature. A cloud of ≈6. 107 samples is generated by random-walk sampling in order to identify the compositional flexibility when aiming for a given set of target properties. In this way, trade-off correlations between physical properties and chemical boundaries of compositional design are revealed.