Abstract
Rapid urbanization and industrial growth have significantly deteriorated air quality in major cities, posing serious risks to public health and environmental sustainability. Accurate and timely prediction of air pollution levels is essential for effective urban planning, policy formulation, and real-time decision-making in smart city environments. Traditional air quality prediction methods often struggle to capture the nonlinear and complex relationships between multiple environmental factors, leading to limited predictive performance. This paper presents a deep learning-based air quality prediction model designed for urban smart cities. The proposed framework integrates historical air pollutant data with meteorological parameters to predict future air quality levels using deep neural network architectures. Emphasis is placed on system design, data preprocessing, model training, and performance evaluation. The results demonstrate that deep learning models offer improved prediction accuracy and adaptability compared to conventional approaches, making them suitable for deployment in intelligent urban monitoring systems.