Wastewater treatment plants rely on diverse microbial communities to remove and recover nutrients, but existing genetic monitoring methods cannot show which microbes are actively doing the work. We developed a new platform that combines single‑cell Raman spectroscopy, which reads the chemical fingerprints of individual cells, with artificial intelligence to profile and track microbial activity in real treatment plants. Using nearly 47,000 single‑cell measurements from facilities across North America, we found that plants perform more reliably when certain specialized microbes are abundant, while too much functional diversity can reduce stability. Our machine‑learning models accurately predicted plant performance, offering a faster and more precise way to monitor and manage these microbial ecosystems for more sustainable wastewater treatment.