It is subjective what you are going to call your "Unseen Data", and it would not matter IMHO. You may call it production data as it is suggested above, or even unseen data or test data. In the Machine Learning Glossary by Google Developers, you find all standard definitions. What I would personally prefer to call is either test data or hold-out data:
Examples intentionally not used ("held out") during training. The
validation dataset and test dataset are examples of holdout data.
Holdout data helps evaluate your model's ability to generalize to data
other than the data it was trained on. The loss on the holdout set
provides a better estimate of the loss on an unseen dataset than does
the loss on the training set.
After all it is the data that is held out and model has not seen, whether intentionally or naturally, that model need to do the predictions on. I have to admit that both these names imply that at present you may own such data, and it is kind of contradictory to the fact that such data is not avaialble yet; but I would still use one of these terms as long as myself and my audience is fully aware of it.