Smarter Planet: AI-Driven Approaches for Real-Time Environmental Monitoring and Decision Making

Abstract Pollution, climate change, and the loss of biodiversity are all major issues that need modern technology that can swiftly provide people relevant information. Latency difficulties, poor coverage, and insufficient predictive abilities are all common drawbacks with traditional monitoring systems. Recent progress in AI, the Internet of Things (IoT), remote sensing, and data-driven modelling has given us an unprecedented chance to build smarter planetary systems. This research introduces an AI-driven system for real time environmental monitoring and decision-making. The suggested system uses optimisation methods, neural networks, and machine learning algorithms to analyse data streams from satellites, sensors, and citizen science platforms. Air and water safety, weather patterns, ecosystem health, and other related problems are all given top priority in predictive analytics. We use open-source datasets to show AI algorithms' accuracy and scalability, test the framework, and analyse policy and long-term growth consequences.