Study and analysis of train dataset along with various ML algorithms is used widely in different sectors. The accuracy parameters can be clarified to have prediction of different score levels. This study covers the extension work of Students’ social engagement during covid-19 pandemic. The study was initiated with students’ social connection during the pandemic. We had compared various machine learning algorithms with its performance about the engagement of students in various social network. After studied, analyzed & compared, we derived that the most of students’ social engagement found in WhatsApp, YouTube & Instagram. The current study is foreseeing age wise social media connection. It correlates between student & their social engagement during the pandemic phase. In which age group, which social media is one of the most popular one. This study focuses on age wise classification using Machine Learning. In this paper, the decision-making classification is compared. The Reduced Error Pruning Tree (REPTree) and Random Forest algorithm is implemented on train dataset with diverse nodes. The attributes are focused as age & time spent on social media as per necessity of study. This paper includes the study and analysis of RAE & RMSE along with ML tree approach. The discoveries of this study can lead better classification in regards of students’ age and duration which they have spent on social media for derived social platform.