Studies have found that exposure to elevated levels of PM2.5 concentrations, both in the long term and short term, has adverse health effects. The risk of exposure in the indoor microenvironment is higher compared to that of outdoor because people spend a significant amount of their time indoors. Therefore to mitigate the health risks, it becomes essential to maintain indoor PM2.5 levels. To do so, keeping an eye on the future expected concentrations is equally important as monitoring the current PM2.5 levels. This paper talks about the development of one such model that enables us to predict the indoor PM2.5 concentrations using Support Vector Regression. Upon development the model showed a very high value of R-square (0.98) and while testing it yielded a very low error (3.78%).