This study explores the potential of Environmental, Social, and Governance (ESG) analytics to be systematically incorporated in portfolio optimization models aiming not only for sustainability goals but also for preserving financial returns. The study uses data on firm, level equity returns from worldwide capital markets and standardized ratings from major data providers to propose an, enhanced mean variance optimization framework that accommodates sustainability concerns in addition to traditional risk return factors. The researchers use sophisticated econometric tools, such as Principal Component Analysis, to formulate a single composite their score and solve the problem of high interdependence among ESG sub, factors. Several performance criteria, including return, volatility, Sharpe ratio, and downside risk, are used to assess the portfolios, and the differences between the portfolio approaches are statistically tested. The data, driven analysis reveals that portfolios that incorporate their factors show less price fluctuation and better risk, adjusted returns than traditional ones, whereas portfolios with ESG restrictions record dramatically increased sustainability ratings but only very slightly lower expected returns. The results strongly indicate that the incorporation of its information helps a portfolio to become more stable by taking into account non, financial risks that are only partially accounted for by conventional financial indicators. The paper makes a contribution to the sustainable finance field by providing a statistically sound and practically feasible method for its, based portfolio construction, thus offering a valuable tool to those institutional investors, asset managers, and policymakers who intend to integrate their financial decisions with the needs of long, term sustainability..