Vehicle emission is a major source of air pollution. A significant number of deaths is associated with air pollution. The existing vehicle emission monitoring system is unable to mitigate the pollution properly. Real-time pollution measurement is necessary to address the problem more precisely. The purpose of this project is to develop novel Vehicle Emission Monitoring Applications (VEMA) using the real-time measurement of pollutants from a vehicle tailpipe where exhaust gases enter the environment. As a part of the project, 2 technologies have been used for exhaust gas monitoring. First, an off-the-shelf exhaust gas monitoring prototype has been acquired. Second, a brand new platform was developed using off-the-shelf components. The project focused on predictive application for smart mobility and for large large fleet companies and enterprises. A data collection methodology was developed and tested by collecting close to 20 hours of data within Washington State under varying speeds, altitudes, road conditions etc. An approach for data curation was also used and finally, a simple Machine Learning was used for recognizing patterns between inputs such as vehicle speed and altitude and output such as NOx emission from vehicle exhaust pipe.