International Journal of Scientific Research and Engineering Development

Air pollution is a critical environmental challenge that significantly affects public health, economic productivity, and overall quality of life, particularly in rapidly urbanizing regions. The Air Quality Index (AQI) provides a standardized mechanism to translate complex pollutant concentration measurements into an easily interpretable scale that communicates health risk levels to the public. With the growing availability of environmental monitoring data, predictive modelling has become essential for forecasting future AQI levels and supporting proactive environmental management. Python has emerged as a leading platform for AQI prediction due to its extensive ecosystem for data processing, statistical analysis, machine learning, deep learning, and visualization. Ensemble learning models such as Random Forest and Extra Trees regressors demonstrate high predictive accuracy and robustness against noisy environmental data. In addition, hybrid deep learning architectures combining Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (Bi-LSTM) networks effectively capture complex spatial and temporal dependencies in air quality datasets. Modern systems emphasize practical deployment using lightweight web frameworks to ensure accessibility and real-time usability. Future advancements are expected through attention mechanisms, transformer architectures, physics-informed learning, uncertainty quantification, and multi-modal data integration to enhance prediction robustness and generalization across diverse geographical regions.

Swetha.B, Dr.R.Praba,"Air Quality Index Prediction Using Python" International Journal of Scientific Research and Engineering Development, V9(2): Page(542-547) Mar-Apr 2026. ISSN: 2581-7175. www.ijsred.com. Published by Scientific and Academic Research Publishing.