Finding out which values lead Random tree to a decision
If your dataset was separable into a series of neat decisions, then a classification and regression tree (CART), would give you the type of solution you're looking for. The price you pay for random forest is that by reducing the variance through generating many random trees, you're also reducing the interpretability of the model significantly. Solutions that can give you local solutions are LIME or you can calculate SHAP values or feature importances, but that gives you importances in the context of the model and may not be useful for the type of decision you're looking to make.