As we pass the 20th anniversary of persistent homology and the 10th anniversary of the deep learning revolution, it is time to take stock. In this talk, I will look back and discuss some of the milestones, i.e. work that embodies the intricate interplay of geometry, topology, and machine learning techniques, with a particular eye towards applications. I hope to convince the audience that, indeed, there is an unreasonable effectiveness to topological methods in general and persistent homology in particular. Next to this retrospective, I will also outline areas of improvement and 'unmet needs', with the goal of providing a vision for an even better future. To this end, I will highlight new research directions that aim to advance geometry, topology, and machine learning.