This paper is based on data analysis by python programming language code, data executed on google collaborator platform with PM, i.e. particulate matter data of NAMP, a nationwide air quality monitoring programme, data available in site of CPCB, central pollution control board. The objective of data analysis was to monitor state wise change of annual average value of particulate matter, PM, for past few years, years as available in the site of CPCB and obtained accordingly. The year wise NAMP data, for 2013 to 2023 as available there were collected and subjected to analysis for understanding year wise status of air pollution influenced by PM value. The sources of PM or particulate matter are vehicular emissions, coal based power plants, construction activities, road dust, biomass and garbage burning, industrial processes etc. So to monitor the PM level obviously matters a lot as presence of PM with higher level can impact on health causing serious respiratory diseases like asthma, bronchitis, also heart disease even stoke. Different types of PM are there depending on diameter such as PM10, PM2.5, PM1, measurement units micrograms per cubic metre. Depending on availability either PM10 or PM2.5 were collected and subjected to analysis defined as PM value. Visualization of yearly change as well as trend of PM level for the year 2026 for each state had been determined with the help of artificial intelligence and machine learning. Line plots, bar plots were obtained to visualize year wise changes and neural network model was used to find current year trend.