Given the capabilities of modern machines and the fact that non-homogenous hardware (GPUs, different processors like in Apple Silicon) is back, the "winning" strategy is to have high-level scripting languages where you can ignore most of the details, which call into hyper-optimized, high performance libraries. For instance, when you're using Scipy, you call into Fortran and C almost interchangeably.
Since most likely you aren't writing full programs in the low-level language, it doesn't need to be a general-purpose language offering the same affordances, say, C++ is supposed to provide.
For numerical/scientific code, Fortran is much, much easier to use, especially for people whose background isn't computer science. Being able to write your code in terms of matrices and arrays rather than "pointer to const restrict" is a godsend when you're programming what is often very difficult mathematical code. When compared to modern C or C++ you won't get any advantage in terms of performance, but you don't lose much either, and in return you get to use a language that's much more suited to the task.
The historical Achilles' heel of Fortran is that it's kind of awkward as a general-purpose language, but that's negated by the "compiled core, interpreted shell" approach that's dominant these days.