Review on IOT Based Environmental Monitoring and Air Quality Prediction System using Machine Learning

Real-time monitoring is crucial for successful abatement of air pollution since it poses serious threats to human health and the environment. The Internet of Things (IoT)-based air pollution monitoring system described in this research uses sensor networks to gather real-time data on pollutants such PM2.5, CO, and NO₂. The information is sent wirelessly transmission for analysis to a cloud platform. Decision trees and support vector machines are two examples of machine learning techniques that are used to identify anomalies and forecast future pollution patterns. The design of the system is described, emphasizing its scalability and adaptability from sensor deployment to data analytics. The system’s capacity to measure pollution levels and produce precise projections is demonstrated by experimental findings from a 30-day urban deployment. The system supports public health and urban planning efforts by providing an affordable, real-time solution for monitoring and forecasting air pollution through the integration of IoT and machine learning.