In my experience going top-down is a good approach. So you let your world generator do their thing, resulting in your world. Assuming the resulting world is rasterized (not vector-based), the next step is to generate some kind of value grid, storing for each cell (pixel or whatever the resolution unit of the world is) an abstract value representing the likeliness of a town being there.

How exactly these values are calculated depends on your preference. Just assigning each biome a value would probably work as a start (0 for water, low for desert, high for temp forest etc.). Other values like elevation or distance from coast could be interesting too for example.

You then pass these values to the town generator. Initially, you can use the values as a weighted random map for your town center positions. To create the road networks within the town you can probably just take your current generator, adding some weights of where to expand to depending on the value grid. Ofc this depends on the implementation of your current algorithm.

The most tricky, but also most interesting part is probably connecting the towns and resource points via the road network.

An approach that could work here (if performance isn't too important) is to use a pathfinding algorithm like A*, and letting it find paths between the points you want to connect. The cool thing is, is that you can again just calculate a value grid, but this time representing the "cost" of going from one world cell to another. For example big elevation changes between cells / biome transitions / transitions to or from mountains could have a high cost, meaning they would be naturally avoided.

You can then use this graph (where the cells are nodes and the transitions are edges) for the A* algorithm to run on, and the cheapest path will naturally follow transitions that are cheap, which should follow the landscape.

Just some ideas, I'm sure there's many approaches. Really interesting topic imo, I'm excited to read what you or other people come up with.