A Data-Driven Framework for Environmental Monitoring: Integrating Machine Learning and Analytics

Effective environmental monitoring and conservation activities are very important, as evidenced by the

quickening rate of environmental degradation and the pressing need for sustainable management of natural resources.

Scale, accuracy, and real-time data gathering are three areas where traditional approaches frequently fall short.

Artificial Intelligence (AI) has become a disruptive force in this setting, providing novel solutions that greatly improve

the capacity to observe, evaluate, and deal with environmental concerns. Artificial intelligence (AI)-driven

technologies, such as machine learning, computer vision, and data analytics, have the potential to completely transform

environmental monitoring by making it possible to gather and analyse enormous amounts of data from a variety of

sources, such as sensor networks, satellite imagery, and citizen science projects. Examining the most recent

developments in AI applications for several environmental domains, including pollution management, biodiversity

conservation, and climate change mitigation, this study investigates the role of AI in advancing environmental

monitoring and conservation efforts. The study also addresses the difficulties and moral issues surrounding the

application of AI in these settings, highlighting the necessity of interdisciplinary cooperation and strong regulatory

frameworks to guarantee the ethical and efficient application of AI technology. The accuracy, mean absolute error

(MAE), and root mean square error (RMSE) of the suggested approach are 96.2%, 0.401, and 0.204, respectively. AI

can help create more resilient and sustainable ecosystems, which will ultimately benefit the world and its people, by

being included into environmental initiatives.