The usage of technology in daily operation within government agencies has become necessities to enhance and improve job performance as it is able to spread, obtain and deliver information easily compared to traditional methods. Most of the study conducted in term of e-government and social media has been focusing on the supply side which are government side as well as in two level of government which are federal government and state government. Moreover, few numbers of local councils such as Kuala Lumpur City Council and Malacca City Council do not have a high number of followers in social media since they are not actively using this medium to communicate with public. It differs from Shah Alam City Council that has a high number of followers in social media and actively use social media as their platform to communicate with public. This study examines the determinants of public engagement in social media platforms at Shah Alam City Council involving three different independent variables which are lead to public engagement in social media platform at Shah Alam City council namely effort expectancy , social influence and performance expectancy. This study adopts a quantitative method approach with a purposive sampling and convenience sampling of 220 respondents in Shah Alam City Council. Data collected were analysed using Pearson correlation test and multiple regression tests. The study focuses on the relationship between public engagement and effort expectancy, social influence as well as performance expectancy together with the most contributing factor that lead to public engagement in social media platform at Shah Alam City Council. This study has discovered that there is a positive relationship between public engagement and effort expectancy, social influence and performance expectancy. Besides, current study has proven that effort expectancy is the most contributing factor that leads to public engagement in social media platform at Shah Alam City Council. In this study, there are a few limitations which are limited scope, limited variables and limited model. Thus for future study, they should expand more their scope, have more variables and include more models in their study.