  Happiness underlines the intuitive constructs of a specified population based
on positive psychological outcomes. It is the cornerstone of the cognitive
skills and exploring university student's happiness has been the essence of the
researchers lately. In this study, we have analyzed the university student's
happiness and its facets using statistical distribution charts; designing
research questions. Furthermore, regression analysis, machine learning, and
clustering algorithms were applied on the world happiness dataset and
university student's dataset for training and testing respectively. Philosophy
was the happiest department while Sociology the saddest; average happiness
score being 2.8 and 2.44 respectively. Pearson coefficient of correlation was
0.74 for Health. Predicted happiness score was 5.2 and the goodness of model
fit was 51%. train and test error being 0.52, 0.47 respectively. On a
Confidence Interval(CI) of 5% p-value was least for Campus Environment(CE) and
University Reputation(UR) and maximum for Extra-curricular Activities(ECA) and
Work Balance(WB) (i.e. 0.184 and 0.228 respectively). RF with Clustering got
the highest accuracy(89%) and F score(0.98) and the least error(17.91%), hence
turned out to be best for our study
