There are many recent and not so recent papers that use ML to "solve" optimization problems, like Learning Combinatorial Optimization Algorithms over Graphs. A very, very good entry to the subject is the survey Machine Learning for Combinatorial Optimization: a Methodological Tour d'Horizon.
In your last sentence you probably ask too much. For optimization problems, there are basically two kinds of approaches, exact and heuristic. For all optimization problems you can think of, both approaches have been suggested. Of course (of course!) no algorithm can beat an exact approach, at least not in terms of solution quality as these - by definition - find the best possible solutions. This is not the case for heuristics, which can be of better or worse quality (but maybe beat the exact methods in terms of runtime, so there is a tradeoff). Therefore, when you ask for ML approaches to beat optimization algorithms, these can beat, at best, other heuristics. And again: An ML approach is (almost always) a heuristic approach, and I would add "yet another heuristic approach". You cannot expect them to beat existing heuristics, but you can be lucky, which is true for any other heuristic.
edit: re-reading your question I conclude that I could not really contribute to an answer.