There are some things, like OLS and logistic regression, that are commonly used for both purposes. But there's a sort of moral distinction between machine learning and statistical inference, driven by whether you consider your key deliverable to be y-hat or beta-hat, that ends up having implications.
For example, I can get pretty preoccupied with multicollinearity or heteroskedasticity when I'm wearing my statistician hat, while they barely qualify as passing diversions when I'm wearing my machine learning engineer hat. If I'm doing ML, I'll happily deliberately bias the model. That would be anathema if I were doing statistical inference.
