<p>In this review article, we present the current state of artificial intelligence and advanced technologies in air and water pollution monitoring in the USA. Recent studies in this field show promising outcomes, including the ability to generate more granular data in real-time, using predictive analytics to identify and prevent pollution, and better modeling of complex exposure. Compared to traditional monitoring systems, these next-generation technologies show improvements in timeliness and sensitivity. Despite these promising developments, some gaps and challenges remain, including calibration and standardization, interoperability, regulatory fragmentation, funding limitations, digital divide, and stakeholder trust, which impede their broad and equitable adoption. This article points to some best-practice examples, conducts a structured, evidence-focused narrative review and comparative assessment of the different technologies using quantitative findings from existing meta-analyses and large comparative studies, and discusses barriers to implementation. The article concludes by providing some recommendations for practice and policy. Recommendations from this review are primarily related to the development of consistent technical standards and specifications, workforce and community engagement, participatory governance, and ethical frameworks to address the responsible use of AI and the implications of data bias with the need for accountability and transparency. To fully harness the potential of next-generation monitoring systems in pursuit of environmental justice, public health protection, and informed policymaking will require further research and concerted efforts in policy and investment to address these issues.</p>