Microfluidics offers transformative advantages over traditional macro-scale techniques by enabling miniaturization and precise control of complex biological environments. In the context of fertility research, microfluidic systems allow for accurate replication of the dynamic, micro-confined regions of the female reproductive tract, opening new avenues to explore the cooperative interactions between sperm and the reproductive tract during fertilization. By recreating physiologically relevant conditions, these platforms enable biomimetic analyses that closely mirror in vivo processes, significantly enhancing our understanding of both sperm function and reproductive tract response. In this talk, I will present our latest work at the intersection of machine learning, microfluidics, and high-resolution microscopy to advance our understanding of reproduction. I will highlight new AI-driven opportunities for automating and quantifying gamete selection and analysis, as well as the development of novel, non-invasive technologies for sperm selection and activation in assisted reproduction. I will also discuss how this research is driving a paradigm shift by uncovering the critical yet long-overlooked role of natural mechanical stimulation in regulating reproductive cell behavior and biochemical activity.