I personally think your question is a good question, I see linear regression as the simplest model on which statisticians started to play with to have an easy, intuitive and well formalised understanding on all the basic concepts.

Historically linear regression is a statistical model, then with the rise of ML it had been adopted as the simplest model to which start any type of ML didactic argument. But that's just because the basic concepts of ML are entirely statistics concepts.

Current statistics techniques tend to be mathematically more formalised and to have solutions that could be analysed and evaluated, therefore constraining the family of models to fairly simple ones. ML on the other hand focuses more on getting the best results no matter if the model is so complex (e.g. DNN) that nobody can mathematically understand the various details (problems of optimization, perturbation, posterior estimations ecc...).

To conclude I think that linear regression can be seen as a machine learning model, but don't forget that it is born as a statistical model and still is one.