The concentrations of airborne particulate matter (PM) is often measured as a mass concentration. However, the other way to express particulate matter is by using the Particle Number Count ([PNC]) concentrations. This study aims to analyse the seasonal variation of airborne particulate matter in terms of [PNC] by using R packages and the Boosted Regression Trees (BRTs) technique. The study was conducted at IOES, Universiti of Malaya in Bachok, Kelantan. The monitoring was important to understand the variability of seasonal effects due to different seasons. In this work, only the datasets for three seasons (Inter Monsoon, North East Monsoon and South-West Monsoon) were analysed involving 25,958 data. The air quality monitoring equipment involved was the particle counter Environment Dust Monitor GRIMM Model 180 and a weather station for recording the meteorological parameters. The data analysis was completed by using R software and its package for evaluating seasonal variability and providing the statistical analysis. The relationship between variables was studied by using the Boosted Regression Tree (BRT) technique. The interaction between independent variables towards the [PNC] in different seasons was discussed. The best setting result of BRT model evaluation R² is 0.22 (North-East Monsoon), 0.87 (Intern monsoon 1), and 0.59 for South West Monsoon which indicated that the model developed is acceptable except for NEM and intern monsoon seasons. Temperature (57 %) and wind direction (67%) were found to be the highest factor influenced by the formation of [PNC] concentrations in this area. Finally, good results indicated that BRT technique is an acceptable way to analysed air pollution data.