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Numerical-relativity surrogate models for both black-hole merger waveformsand remnants have emerged as important tools in gravitational-wave astronomy.While producing very accurate predictions, their applicability is limited tothe region of the parameter space where numerical-relativity simulations areavailable and computationally feasible. Notably, this excludes extreme massratios. We present a machine-learning approach to extend the validity ofexisting and future numerical-relativity surrogate models toward thetest-particle limit, targeting in particular the mass and spin of post-mergerblack-hole remnants. Our model is trained on both numerical-relativitysimulations at comparable masses and analytical predictions at extreme massratios. We extend the gaussian-process-regression model NRSur7dq4Remnant,validate its performance via cross validation, and test its accuracy againstadditional numerical-relativity runs. Our fit, which we dubNRSur7dq4EmriRemnant, reaches an accuracy that is comparable to or higher thanthat of existing remnant models while providing robust predictions forarbitrary mass ratios.

Extending black-hole remnant surrogate models to extreme mass ratios