This study develops an integrated value investing optimization model grounded in Buffett-style fundamental analysis to enhance long-term portfolio performance. The research conceptualizes value investing not merely as a heuristic stock selection strategy but as a structured decision-support system that integrates intrinsic value estimation, risk-adjusted optimization, and behavioral consistency mechanisms. Drawing on interdisciplinary insights from decision theory, service quality evaluation, and sustainability-oriented logistics research, the study constructs a hybrid analytical framework that aligns financial decision-making with long-term sustainability logic. The model incorporates fundamental financial indicators, qualitative firm attributes, and macroeconomic stability factors into a unified optimization structure designed to minimize mispricing error while maximizing compounded returns. The study further explores how behavioral and systemic biases influence investment outcomes and how structured frameworks can mitigate such distortions. Empirical synthesis from prior literature supports the argument that disciplined, structured decision systems improve performance consistency. The findings suggest that integrating Buffett-style principles with optimization modeling enhances portfolio robustness, reduces volatility exposure, and improves long-term capital allocation efficiency. The study contributes to investment theory by formalizing value investing as an adaptive optimization architecture suitable for modern data-driven financial environments.