Your question is somewhat unclear, but it sounds like you may be interested in CART: Classification and Regression Trees. Since Cosma Shalizi's lecture notes have a wealth of information, which I won't bother to reproduce here.
The basic idea, however, is that you can put whatever you want on the leaves. They can be binary decisions (i.e., in-class vs. out-of-class), a real value (e.g., 19.73). In theory, you could even let the decision tree choose between multiple regression models.
Similarly, the internal nodes can be pretty flexible. Traditionally, they're binary decisions, but they can be binary decisions on integers or real values (e.g., if $X>22.8$, do one thing, otherwise, do another). There are some extensions that do multiple splits at each node--I think one is called CHAID. You may also want to check out a related technique called MARS, which reportedly works well for all-numeric data. I haven't played with it much, but there are open source packages for python, matlab, and R.