ML has both of those things. In fact the situation is better than in many other sciences because the evaluation is completely digital. When you run an ML experiment you get back an objective measure in minutes or hours telling you if the model performed better or worse. If it's worse, boom, falsified. Many current papers are making predictive testable claims -- that based on various things we know what ideal parameter values are, the types of problems current NN archs will perform well on or poorly on, etc.
We've got lots of datasets at this point in history - it ends up being pretty reasonable to formulate a hypothesis, propose a change to the ML system, then see how it does on a host of datasets. Most papers focus on one or two datasets, getting some initial improvement going from train-to-test, but the successful ideas end up transferring to a wide range of contexts. The extension to other dataseets + contexts constitutes the prediction of performance on other systems.
And sometimes (often?) techniques do fail to transfer! IME, it's only the simplest ideas which do transfer to new contexts well - there's a lot of dark heat in pushing Imagenet another hundredth of a point, which you realize when you try to take incremental 'new SOTA' papers and apply them to audio problems.
But the things which /do/ transfer constitute real advances. And that's (drumroll) science! Sometimes things look good initially and get falsified. Medicine is still science, even if drugs get disqualified in phase iii trials...
Predictability means that if your hypothesis is correct then you'd be able to formulate other improbable (and ideally currently untestable) predictions from it. And falsifiability means that if these predictions fail to occur then your initial hypothesis was also almost certainly wrong. So for instance Newton's hypothesis was that gravity was driven by a mathematical relationship between the mass/distance of two bodies. It was good science because it lead to the shocking ability to be able to dramatically simplify orbital dynamics, and create a complete predictive system of these bodies. It was even used to mathematically discover a completely unknown planet - Neptune.
Incidentally, his theory would also be able to be shown to be false if any of these unexpected predictions ended up being false. And that's actually exactly what happened. Observation of Mercury's orbit about the Sun showed it was off by ~1/3600th of one degree per century, relative to what was expected. And it's from there that people knew there was a mistake, which would only be explained later by Einstein hundreds of years later, who hypothesized a system with far more absurd predictions... and so the story continues.