Regression for discrete values? Im a noob in ml / statistical algorithm, but I do have worked with simple classifiers and regression

I like some opinions if I am going the right way, given my limited knowledge

My problem is following: I wanna best predict price of a input item, given the known price of the most similar items in database.

This at first sounded like regression but my features are mixed discrete and contiguous, (like item model type (think car model, not ml model) is discrete, year of manufactury is contiguous?)

Now I am thinking finding x closest matches via euclidean distance, and then weighted average + somehow make sure the certain features have to match exactly (like item model type)

Thanks
You can still perform regression even if your input (or part of it) is discrete.
If you think of it, even your "continues" values are actually discrete (starting from their initial measurement accuracy/resolution).

You need to decide if you want to train a model to perform estimation, or just cast your input features to a vector space and use them with some euclidean distance (to make sure some features match exactly, just add some "if" check so you can calculate distances only to relevant samples).

To train a model, it should except as input all the features (preferably normalized), and output a single number which you can later round to the closest discrete value (if you like).