CUDA allows different programming models, it's the point of having the PTX virtual machine as the exposed programming model.
CUDA C++ is popular, but is far from the only option people have.
(and it can go very deep, https://www.ibm.com/docs/en/sdk-java-technology/8?topic=egpu... for example)
FORTRAN has tighter restrictions around what you can do in terms of aliasing/etc, which allows compilers to be more aggressive about optimizations without as much undefined-behavior "this looks correct and will run fine until we add another optimization and it doesn't" shenanigans. So in practice it can be faster than C, and it's actually quite popular among the HPC community and other performance-sensitive segments.
It's not like C has to be this way, with all the "undefined behavior" nonsense, it just is an artifact of a programming language written in the early 70s. C makes a lot of sense when a compiler looks a lot more like an assembler with a bit of optimization sprinkled in, and isn't performing super deep introspection and deciding that this entire function can be optimized away because of some arcane "undefined behavior" rule.
I'd say that the most popular option at the end-user level today is a whole level of abstraction above: using Python w/ PyTorch (or less frequently, TensorFlow).
Combined with CuPy and cuNumeric, which is available at https://developer.nvidia.com/cunumeric and handles scaling to multiple machines with Python-written code, through the Legate runtime (https://nv-legate.github.io/legate.core/README.html). This covers a huge amount of what the CUDA userbase uses.