Yes, it's certainly true that RFs can't extrapolate to regions it has not been trained on (or rather, it extrapolates in a very crude way). I suppose it's a philosophical question whether you think that's a bug or a feature. For example, you could argue that a higher order regression is much worse because it likely behaves extreme in those areas without data. Personally, I'd rather have a constant prediction when I go to the edges of the known.
As to your question, any linear model will extrapolate (Ridge, Elastic Net etc), as will SVM regression and Neural Nets. These last two might be the most powerful tools at your disposal with your desired feature. All the decision-tree based methods like RFs or Boosted Regression Trees will not extrapolate.
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