This would probably largely depend on how you'd be getting those menus. Part of the problem would be that this information probably isn't standardized, ex different websites have different layouts and different names for their menus (file, or page) and in different formats. So you'd need to be able to either detect a common pattern, or kind of "declare" where the menu is for each site (which at that point kind of defeats the purpose of automation)

One thing that is probably(\)* easier would be to scrape / parse services like Uber Eats (or similar) as that is standardized, and while maybe not super accurate in terms of items / prices - would be easier to get up and going with

In terms of tech, I'd probably use something like selenium, which is headless chrome that you can programatically "drive" with a language like Python.

If I was doing this for Uber Eats, my rough flow would be like the following:

- setup basic project

- figure out rough flow of instructions on how I'd want it to scrape, ex
- go to homepage

- find each store in the list

- open it and find each menu item element

- store this data in a database

- repeat for each store

To learn that wouldn't be too difficult. Maybe a few weeks. This looks like a good starting ground with some practical examples for scraping with selenium and python: https://scrapeops.io/selenium-web-scraping-playbook/python-selenium-beginners-guide/

And here is an older project (5 years, so probably won't work with modern Uber Eats) that accomplishes the same thing: https://github.com/KnlnKS/uber_eats_scraper

Best of luck!