The advent of Artificial Intelligence (AI) is revolutionizing the
environmental monitoring systems processes, as well as the management of natural
resources and environmental threats. This project assesses how AI technologies are
being utilized in all environmental sectors for the purpose of monitoring. From the
monitoring of air quality to forecasting climate change, AI provides decision makers
with the available data and information in real time. AI, together with IoT and Big
Data, allows the creation of solutions that improve data intake and processing in time to
avert any unsustainable practices. Environmental assessment accuracy and efficiency
are enhanced through AI’s superior machine learning algorithms, predictive analytics,
and computer vision. The study examines the use of AI in monitoring air quality and
managing water resources; tracking wildlife and climate change, and presents examples
of technology installations. The existing technologies have their limitations, and thus,
as a result, deployment of AI for environmental monitoring comes with criticism and
concern, which include the quality of the data, ethics, and other limitations. In detail,
the future is bright regarding Artificial Intelligence monitoring systems applied in the
environmental sectors due to several developments and innovations in technology. The
project calls for the need for practical global solidarity with appropriate structures in
place because the challenge of AI for growth, renewal, and sustainability needs wellthought-out policies. Considering a shift towards a circular economy, AI has the
potential to revolutionize environmental monitoring, helping ecosystems to flourish
alongside human ambition. Several significant outcomes are expected from this project.
Chief among these is the provision of input and advice to relevant actors on how best to
utilize AI in the pursuit of environmental management objectives.
Keywords: Artificial intelligence, Cloud computing, Computer vision, Decision making, Edge AI, Environmental monitoring, Internet of things, Machine learning, Sustainability.
