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.