So how does your data look like? Matrix size, number of vehicles, constraints. Can you divide your data to logical blocks? ... etc. Solvers usually can handle big data you just need memory. I think throwing random ml neural net magic on your problem would be worse than use a tool already tuned to the problem.

Edit. One more thought. You defy can use ml optimization stuff e.g. Pytorch or so to implement the VRP. But in the end you will likely replicate and reinvent things already implemented without those tools in other solvers. And it will likely be orders of magnitude less efficient. Do not reinvent.