## 2 Related Works  
Currently, the use of machine learning as a forecasting tool in various fields is on the rise, and the field of air quality has yet to be an exception. Several related research studies have been conducted in different countries; in their research, [PERSON] et al. [6] utilized machine learning techniques to predict ground-level ozone concentrations in Mexico City using hourly data from March 2015 to February 2016 and aimed to elucidate the relationship between variables and high ozone levels. The performance of three distinct models, Artificial Neural Network (ANN), Support Vector Regression (SVR), and Random Forest (RF), was evaluated based on the coefficient of determination (\(R^{2}\)) and the index of agreement (IOA); [PERSON] et al. [7] conducted a study analyzing air pollution data from various cities in India, encompassing nine attributes, including location details and pollutant levels. The study focused on daily data of pollutants, such as \(SO_{2}\), \(NO_{2}\), \(PM_{10}\), \(PM_{2.5}\), \(CO\), and \(O_{3}\), aiming to forecast air pollution levels for the subsequent days. Six supervised machine learning techniques were used to build predictive models, including logistic regression, support vector machines, random forests, K-nearest neighbors, naive Bayes classifier, and decision tree. The research emphasized learning and predicting the air quality index through adaptable machine learning algorithms, with Accuracy as a metric for model evaluation; [PERSON] et al. [8] conducted a study based on data collected by Taiwan's Environmental Protection Administration (EPA) from 2008 to 2018, focusing on three specific regions within Taiwan. The primary objective was to develop models to efficiently forecast the Air Quality Index (AQI) for short-term durations: 1 h, 8 h, and 24 h. Five machine learning algorithms, including random forest, AdaBoost, support vector machine, artificial neural network, and stacking ensemble methods, were examined to achieve this. Model performance was evaluated using scale-dependent error indexes: MAE, RMSE, and \(R^{2}\). In a study led by [PERSON] et al. [9], data from the Jordanian Ministry of Environment, covering the period from 1 May 2014 to 4 June 2019, were analyzed. This dataset captured daily averages of ozone readings alongside meteorological variables, such as temperature and wind patterns. The study's objective was to forecast the daily ozone concentration in Amman by leveraging a blend of meteorological and seasonal indicators from the preceding day, including distinct events like special days. The study evaluated several algorithms, namely, MLP, SVR, DTR, and XGBoost, and found MLP to be the most proficient. Additionally, the team explored the potential outcome improvement by integrating smoothing filters into the time-series data. The efficacy of the models was gauged using metrics like MAE, RMSE, and \(R^{2}\). In research conducted by [PERSON] et al. [10], data from various regions within the United States, spanning 1 January 2000 to 31 December 2015, were examined. The study employed three machine learning algorithms, neural network, random forest, and gradient boosting, to model \(PM_{2.5}\) levels. These algorithms incorporated a broad range of predictor variables, encompassing satellite data, meteorological factors, land-use metrics, elevation, chemical transport model predictions, and several reanalysis datasets. An ensemble approach was adopted to amalgamate the outcomes of these three algorithms, yielding a consolidated \(PM_{2.5}\) forecast. The models' effectiveness was gauged using metrics such as \(R^{2}\), RMSE, MAE, and mean bias error (MBE). In their comprehensive study, [PERSON] et al. [11] harnessed meteorological parameters, including vertical wind, wind speed and direction, temperature, and relative humidity, to predict concentrations of pollutants like \(CO\), \(NO_{2}\), \(O_{3}\), \(SO_{2}\), \(PM_{10}\), and \(PM_{2.5}\) across three strategic sites in New Delhi's districts.