> one record close to another in x, y, z will likely have a similar outcome

That sounds a lot like k-nearest neighbors, or SVM with RBF kernel. Might be worth giving those a shot. That said, xgboost is effective on a wide range of problems so I wouldn't be surprised if it's tough to beat. Under the hood I'm sure it's learning approximated bounding boxes for your classes.

I haven't heard of CNNs being used for this kind of problem. I've more seen CNNs for spatial processing when the data is represented differently, for example if each input were a 3d shape represented by a 3d tensor rather than coordinates.