Basically, it was mainly inhertia. Older professors that liked it and rarly used anything else and the fact that generally no one gets rewarded for actually rewriting parts of an existing functioning course.

As an instructor you basically create more work for yourself in the first time you migrate a course's programming language. (And you also annoy some senior staff when forcing them to learn new things)

If you're more productive in Matlab, that's fine. But if you're at a loss without it, that's not.

It doesn't belong in the education system or in educational books.

It is also not true today that not knowing Matlab harms your industry productivity in ML. This might have been true around a decade ago, but most teams outside academia also have moved to non-Matlab resources. And if anything, this has been further reinforced by Deep Learning libraries, the current crop of MLOps tools and cloud-based frameworks.

Matlab might be good for specific areas, but ML has not been a stronghold for a while. It is also important to remember that in the context of numerical accuracy or computation speed, Python is almost always the user-facing layer. You might (correctly) argue that the Python language is slower/faster than X, but this is not a useful metric for comparing libraries and frameworks, where the compute heavy code is probably in C/C++: numpy, tensorflow, pytorch are good examples of this.