In the current decade, presence of air pollution leads towards serious health conditions, including respiratory ailments, cardiovascular disorders, and lung cancer, which impacts both the lifespan and overall well-being of individuals. Moreover, the air pollution has the potential to have detrimental effects on the environment, resulting in destruction to ecosystems, decreased agricultural output and so on. Emission control systems, better fuels, stronger laws, energy efficiency improvements, and renewable energy promotion are helping industries reduce air pollution. To monitor the quality of air methods such as Particulate Matter (PM2.5), PM10, Ozone (O3), and other meteorological indicators exists. These measures pose real-time air quality however it fails to predict air quality. Predicting air quality helps reduce air pollution by enabling timely interventions and preventive measures to mitigate pollution peaks. Thus, in this research a hybrid version of optimization algorithm namely Hybrid Air Quality Prediction system (HYAQP) which is a combination of k-means clustering algorithm and meta-heuristic algorithm Sine Cosine Algorithm (SCA) is proposed. The HYAQP holds SCA integrated with k-means algorithm to find optimal cluster centroid for grouping the air data into three clusters good, poor, and moderate quality. Then the cluster which is nearer to the test instance is found and the instances present in those clusters are passed to K-Nearest Neighbor Regressor (K-NNR). Comparing HYAQP on mean absolute error it outperforms 62.9% than Multiple Linear Regression (MLR), 58.5% than Support Vector Regression (SVR), 45.5% than Vanilla-Long Short-Term Memory (Vanilla-LSTM), 44.4% than Sparrow Search Algorithm based-LSTM (SSA-LSTM) and 53.8% than K-Nearest Neighbor (KNN).

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