Can't really help you with how you would achieve this using machine learning; But I can recommend you take a look at surface fitting techniques such as gaussian process regression? They're the tools I would likely reach for when addressing such problem

Is the input space very large?

What if you train a model to predict the actual value using your dataset and then simply run an exhaustive prediction over your input space? Then lookup the closest model output value whenever you need it and check the corresponding input parameters.

sorry, we don't answer homework questions here

Bayesian optimisation

Response surface methodology

It is actually not a homework. I'm taking part in a new project that I never worked on before and I justed wanted to brainstorm here with the community since I don't know where to start. I'm not asking for a particular solution but rather looking for direction in terms methods from experienced people.

The input space is in the order of millions, so I think that is still a valid approach.

I was actually thinking about doing the same but I'm not sure how accurate the results of such model would be. I think the accuracy here will be limited with the granularity of the input space.

Thanks I will take a look at the response surface methodology since I'm not really familiar with

Thanks for the suggestion. I will take a look.