This research's main goal is to provide an in-depth framework for optimizing happiness through combining psychometric evalutation, factor mapping, linear regression, and computational optimization. Based of the six major factors in the World Happiness Report determining happiness: GDP per Capita, Social Support, Healthy Life Expectancy, Freedom to Make Life Choices, Perceptions of Corruption, and Generosity. This model tries to dissect these major or primary factors into sub-factors through Exploratory Factor Analysis on basis of survey responses. The sub-factors are then used to build a predictive happiness using a linear regression model, which was numerically optimised to find the maximum happiness. Our results present both empirical confirmation of happiness drivers and actionable optimisation targets, illustrating how data science techniques can bridge psychological theory and real-world interventions in well-being.