Abstract
Rapid industrialization, urban expansion, increasing vehicular emissions, and population growth have significantly deteriorated air quality in modern urban environments. Air pollution has emerged as one of the most serious environmental and public health concerns worldwide, contributing to respiratory diseases, cardiovascular disorders, reduced life expectancy, and ecological degradation. Conventional air quality monitoring systems primarily depend on fixed monitoring stations and laboratory-based analysis methods, which are often expensive, limited in spatial coverage, and incapable of providing real-time predictive insights. Recent advancements in Artificial Intelligence (AI), Internet of Things (IoT), wireless sensor networks, and cloud computing technologies have enabled the development of intelligent environmental monitoring systems capable of continuous pollution assessment and forecasting. AI-driven systems can analyze large-scale environmental datasets, identify hidden pollution patterns, and predict future air quality conditions based on historical and real time data. This paper presents an AI-based air quality monitoring and pollution forecasting framework designed for smart city environments. The proposed system integrates distributed environmental sensors, cloud-based analytics, machine learning prediction models, and intelligent alert mechanisms to support proactive pollution management. The framework continuously monitors major air quality parameters including PM2.5, PM10, carbon monoxide, sulfur dioxide, nitrogen oxides, temperature, and humidity. Experimental analysis demonstrates that the proposed framework improves forecasting accuracy, pollution detection efficiency, and environmental decision-making capability compared to traditional monitoring approaches. The study contributes toward sustainable urban development and intelligent environmental governance in future smart cities.