Air pollution forecasting plays a vital role in safeguarding public health and guiding environmental policies. Traditional prediction models often fail to accurately capture the temporal and spatial complexity inherent in air quality data. This study presents an AI-enhanced solution that leverages a 1D Convolutional Neural Network (1D-CNN) optimized using the Kookaburra algorithm for more precise air quality forecasting. The proposed model is specifically designed to extract significant patterns from time-series data while minimizing prediction errors through adaptive hyperparameter tuning. The integration of a biologically inspired optimization technique with a deep learning framework yields improved performance across key metrics such as accuracy and generalization. This hybrid model not only demonstrates computational efficiency but also offers practical applicability for real-time environmental monitoring. Future research may extend this framework to incorporate IoT sensor integration and hybrid attention-based models for even more robust predictions.

Air Quality Forecasting, 1D-CNN, Kookaburra Optimization, Deep Learning, Environmental Monitoring, Public Health, AI/ML