Roughly half the 'power of prolog' comes from the 'power of logic programming' and prolog is by far not the only logic programming language, e.g.,

- You can do logic programming using minikanren in scheme. (you can also extend the minikanren system if you find a feature missing).

- Minikanren was implemented in clojure and called core.logic.

- It was also ported to python by Matthew Rocklin I think, called logpy.

- There is also datalog, with pydatalog it's python equivalent.

- Also pyke. And so on.

Plus logic programming has very important (arguably necessary) extensions in the form of constraint-logic-programming (CLP), inductive-logic-programming (ILP), and so on.

It's a huge area.

EDIT: ILP at an advanced level starts making connections with PGMs (probabilistic graphical models) and hence machine learning, but its a long way to go for me (in terms of learning) before I start to make sense of all these puzzle pieces.

EDIT 2: You can have a taste of logic programming without leaving your favorite programming language. Just try to solve the zebra puzzle [1] (without help if you can, esp. through any of Norvig posts or videos; they're addictive).

[1] https://en.wikipedia.org/wiki/Zebra_Puzzle

EDIT 3: An "expert system" (a term from the 1980s) is largely a logic programming system paired up with a huge database of facts (and probably some way of growing the database by providing an entry method to non-programmer domain experts).

EDIT 4: In other words, logic programming (along with other things like graph search etc) is at the core of GOFAI (good old fashioned AI), the pre-machine-learning form of AI, chess engine being a preeminent example of it.