Biological monitoring (biomonitoring), specifically the collection, identification, and enumeration of animals, in aquatic systems is essential for understanding ecosystem health. These receptors are key indicators to understanding shifting environmental conditions, including threats to human safety. However, biomonitoring is labor-intensive and requires considerable equipment, resources, and taxonomic expertise. As such, methodological paradigms for aquatic organismal field collection and subsequent laboratory sample processing have largely remained stagnant for the past several decades, which, ultimately, has resulted in limited spatiotemporal richness of biomonitoring information relative to other recent technologies that provide higher volumes of data (e.g., remote sensing, water quality). Artificial intelligence (AI) stands to revolutionize biomonitoring, ranging from advancements in computer vision and image recognition to full automation of sample processing or collection. Here, we present an overview of 7+ years of research on advancing novel optical hardware, machine learning, and automation strategies in improving the rate and accuracy of collection, identification, and sample processing of benthic macroinvertebrate, zooplankton, and larval fish. We provide a series of case study applications including sampling benthic invertebrates in streams to sampling larval fish and planktonic communities in reservoirs and coastal areas. Case studies will include applications of how AI or automation has been applied to 1) organism identification and enumeration, 2) expediting sample sorting, i.e., differentiating target organisms from bycatch, 3) standardizing nationwide biotic integrity procedures, 4) in situ field applications of real-time time biomonitoring, 5) early detection of invasive species, and 6) designing and testing prototypical robotic systems for autonomous sample collection and identification. We also discuss some of the challenges of applications in AI, including optical hardware limitations, variant sources of imagery for model training versus prediction, and the unrecognized time investments needed to develop and advance the use of AI technologies in aquatic biomonitoring.