Air Quality Forecasting: This study explores data-driven techniques and advanced sensing technologies to improve air quality prediction. Focusing on deep learning models like LSTM, GRU, and DeepAR, the research evaluates their effectiveness in capturing dependencies among pollutants for accurate forecasts. The paper presents a comparative analysis of these models, highlighting the bi-directional GRU’s superior performance in time series data processing. Future work suggests expanding datasets, exploring CNN techniques, and integrating external factors to enhance predictive accuracy.