The integration of environmental, social, and governance (ESG) factors into corporate strategy has become a central element of financial decision-making. This study examines how ESG performance influences investor preferences and financial outcomes through a computational and data-driven framework. Utilizing a convergent computational framework, the research combines ESG performance data from 500 publicly listed firms with survey responses from 300 investors, split equally between institutional and individual participants. Using advanced exploratory factor analysis (EFA), correlation analysis, and robust regression modeling (at model level and equation level) the paper investigates the independent and simultaneous effects of the ESG dimensions on investor choices. Results indicate a dominant effect of environmental and governance modules on investor reactions, with a very large increase in preference for environmental factors before the level of initial saliency decreases. Sociological components indicate non-linear effects analyzed through computational models, suggesting that balanced ESG practices are most effective in inducing investment interest. The financial efficiency ratios derived from algorithmic processing also suggest that, beyond absolute financial size advantages accruing to high EG performers, higher per-unit returns were often observed in the mid-portion of the ESG commitment range. Findings highlight the strategic merit of computationally enhanced ESG integration into corporate and investment strategies. Additionally, the paper provides a computational methodology for assessing ESG efficiency and advancing the understanding of how sustainability investments generate marginal return. These findings offer an evidence-based and computationally validated foundation for connecting sustainability performance with long-term financial achievement.