How to use features when predicting aggregation?
You can pass the entire month's data as a single sample, which should predict the sales of that month. It would look like an index with multiple indexes. Something like MultiIndex in python's Pandas library.

Another idea would be to still try predicting the sales every day, then just sum up all predictions at the end to create your monthly aggregation.

Using something like a neural network may be overkill - it depends how much data you have - but will offer the flexibility to easily train a model with multiple inputs and a single output, as is your case.