Indoor air quality is a measure of clean air with comfort conditions and depiction of lower concentration of air pollutants. It is tedious task to achieve all quality measures at a time with smart energy consumption. This research aims to come up with a solution of how to improve smart ventilation system in order to get clean indoor air with less consumption of electric energy. Many studies showed that scheduled ventilation system has proven to be a good solution to this problem. For this purpose, a long-term sensor data of smart ventilation system Renson healthbox and Luvians data is studied which is operated in Dalarnas villa. This research investigates how this system works in two modes and to improve it by customized scheduling.A regression model is constructed in which the relationship between airflow and CO2 is shown. For this purpose, correlation analysis is used in which the connection of bonds between each data features are analyzed. After the feature selection, as a result from correlation matrix, regression analysis is used to find out whether the selected features are linearly related or not. Regression analysis also used for the intent to quantify a model to estimate the flowrate and CO2. A mathematical model is also build to simulate the flowrate and CO2 with energy consumption.The results showed that, in order to provide better indoor air quality with efficient energy consumption, a necessary modification of the fan schedule should be done in a way that fan must be started little bit earlier to avoid harmful particles reach their upper threshold limits. This can result in reduction of fan’s maximum speed hence consumption of less energy is achieved.