A high absolute difference between heading and course can give you a hint of the weather conditions, but that hint is not very useful: it's windy and it blows from the side.
But more seriously though: I think I see what you are getting at, but to predict plane's track for meaningful distances on momentary observations of weather only is not very straightforward. Apart from certain areas in the world, weather usually is not very stable. The algrorithm or neural network doing the prediction would have to constantly update it's "knowledge" of the surroundings to keep up. And it needs a lot of data to figure out that knowledge.
A short version: my less educated guess would be, that asking such an application "check how things are now, and tell me where I will be after flying for 200nm" will yield rather useless answers.
And the longer one: Basic rules to deduce weather pattern from conditions prevailing for, say, 15 minutes should not be all too bad. As JohnK earlier mentioned, you would need multiple data sources: accurate 3d location, airspeed, temperature, heading, pressure, and then measure trends for these. Actually you'd need to know the date too... You'd get a data set that can be used to determine which weather pattern fits best to the conditions (listing the mnemonics on weather patterns would take an hour, so I'll skip that. I've always hated meteorology for that...).
The "fluid" nature of weather is what makes what you are after difficult. If you need a prediction for the track of the plane, it really is not a prediction if it updates constantly. Predicting track from a single snapshot of condition is highly unreliable for longer distances, and thus pretty much useless, at least in my opinion, and somewhat experience too.