<p>Solid electrolytes have emerged as a promising alternative to conventional liquid electrolytes that have posed safety concerns. However, the relatively low ionic conductivity of solid electrolytes is one of the challenges for their practical application. In this work, we study high-entropy materials over a wide chemical space as a combinatorial strategy to enhance the ionic conductivity of solid electrolytes by up to orders of magnitude. To this end, we developed an iterative search framework that efficiently identifies chemically compatible substitutional elements to systematically generate 113,098 high-entropy materials, starting from 93 known Li-ion conductors as structural prototypes. By progressively identifying and substituting elements, our scheme enabled efficient and comprehensive coverage of the vast high-entropy compositional space. The generated candidates were then systematically screened using a combination of machine learning models and density functional theory calculations to assess key properties. Through this approach, we identified eight halide candidates with exceptional ionic conductivities, which were further validated via molecular dynamics simulations. This work highlights the potential of the high-entropy approach as an effective optimization strategy for solid electrolytes.</p>