What should I study to find optimal value of best feature combinations in machine learning? I would like to do production optimization with machine learning and/or optimization problem.

My goal is not to find minimizing loss in loss function only to give the best y value. My ultimate goal is to find optimal values of the best feature combination. In other words, 'I would like to find the best x values (feature values) that yields the best y (minimizing loss). For example, 30 (temp), 100 (Pressure), 50 (weight) gave the best product y. The actual best set of x feature values. 

Supervised machine learning techniques do not provide the best-predicted x values to me. but I would like to find the x. Therefore, my question is as below:


What is the field for finding optimal x values for optimal y values called? Is continuous and discrete optimizations are ones finding the x values for y?
I found many studies but I am not sure if it is really about finding the x values (the optimal values of the best combination of features). Can someone please tell me what field I must study for this? 
What kind of techniques should I use to find what I want?
Welcome to DS.SE!

As you already mentioned, machine learning is not about finding the best values of features regarding a function. Also, loss functions usually measure the errors of predictions and not the quality of the feature values (e.g. " the best product y").

What you are looking for is called optimization or search algorithms but not learning or prediction. If the search space (i.e. the combination of all possible feature values) is not that large, you can just perform an exhaustive search (trying all combination). If the search space is large, then smarter approaches might be useful. These techniques need a function (usually called objective function) to optimize (finding the combination of input values that results in a min/max outcome). 

In general, there are two approaches: mathematical modeling (i.e. linear programming) and metaheuristics (e.g., evolutionary algorithms and swarm intelligence). Mathematical models can be fast and guarantee to find the optimum when the objective function is well-defined and the underlying assumption are met (you may need to simplify your constraints sometimes). If these conditions are not met, then metaheuristics usually can find a set of approximate solutions.