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> fastest and most efficient method (time and memory-wise)

Time and memory go against each other in a simple way: amount of jobs divides the time (up to amount of cores, sans the overhead, assuming no I/O bottlenecks e.g. an SSD), and multiplies the peak memory used.

> stored in a list named paths

Is that a Python list, or do you mean a newline-delimited file?

For the latter, an easy bash way to parallelize is `xargs --max-procs=16 --max-args=1 command_to_convert_one < pathlist_file`

Alternatively, to list all files recursively (just in case), you can use `find Main_dir -type f -name '*.npy'`.

> convert all these `.npy` files to image

Here is the most unclear part of your question: npy files are datafiles (claimed as alternative to CSV).

There are many ways to convert them to images, such as plotting the data with matplotlib or any other library; or interpreting the data as image pixels; or anything else.

Without knowing what data is there and what you are trying to do, it is impossible to give a good answer.