Oh that bit does make sense, there's a natural gravitation towards it and the more people use it the better it gets. Have there been attempts to recreate a similar ecosystem in other languages though.
Python is widely adapted not really because the language itself is any good or particularly performant (it's not at all), but because it presents easy to use wrapper APIs to developers who may have a poor background in Computer Science, but are rather stronger in statistics, general data analysis, or applied fields like economics.
MATLAB is a proprietary computing platform with its own language that was very widely used (and probably still the standard in some fields of engineering). The fact that it is proprietary and the language is not great as a general programming language were significant drawbacks to wide adoption.
As far as ML is concerned, the deep learning revolution happened when Python was the dominant language for scientific computing (mostly due to NumPy and SciPy), so naturally a lot of the ecosystem was built to have Python as the main (scripting) language. The rest is history.
As far as "attempts to recreate similar ecosystem":
PyTorch (currently the most popular deep learning framework), was originally Torch (initial release: 2002 - long before "Deep Learning" was a thing) with Lua as the scripting language. Python's momentum in 2010's meant that eventually it was rewritten in Python thus becoming PyTorch.
Julia language is a famous somewhat recent example (first release 2012, stable 1.0 in 2018) of a language that was built partially to address some of Python's shortcomings and to "replace" it as the default for scientific computing. It didn't succeed - it's hard to move people away from an ecosystem with so much head start and momentum as Python had in the 2010s.