Air pollution, a harmful or excessive quantity of pollutants from natural sources and human activities, poses risks to human health, the environment, and ecosystems. AI breakthroughs have allowed for the incorporation of technologies into performance indices, resulting in the development of an AI-based air quality system that evaluates water quality in real time using WHO-defined parameters. This article describes the implementation and planning of AI-based IoT for air pollution tracking and forecasting utilizing AI methodologies, as well as a dashboard on the internet for real-time tracking of air pollutants via Google Cloud servers. Air pollutants such as NO2, NOx, NH3, CO, SO2, and O3 are gathered from IoT sensor nodes in Sivakasi, Tamil Nadu, India, utilizing artificial intelligence algorithms. Individual pollutants are forecasted using time series modeling approaches such as Artificial Neural Network (ANN), Naive Bayes Model, k-nearest neighbour (k-NN), Support Vector Machine (SVM), and Seasonal Autoregressive Interated Moving Average (SARIMA). The data from the IoT sensor node is utilized to train the model, resulting in optimal parameters. The derived model parameters are validated using new, previously unknown data for time. The performances of several Time Series models are examined using performance metrics such as Mean Absolute Error (MAE), coefficient of determination (R2), and Root Mean Square Error (RMSE). An AI-based algorithm has been flashed in the Raspberry Pi 3. The present air pollution data and anticipated data are monitored throughout a 7days from 10 p.m. to 4 a.m. using a digital dashboard built in an open-source using Google cloud services. Finally comparing to all above AI based algorithms, SARIMA performed well and h+ad a 95% accuracy level.