The rapid growth of corporate pension funds shows a rising dependence on private pensions, as public pensions alone cannot provide enough income for retirement. However, the Mean-Variance Optimization (MVO) model, which is commonly used in managing private pensions, has clear limitations in handling multi-period investment needs, particularly in aligning long-term retirement goals with dynamic market conditions. This study proposes a goal-based dynamic asset allocation model that incorporates income replacement rates to evaluate whether individuals can achieve stable retirement income through consistent, tax-deductible contributions. The model employs dynamic programming techniques to optimize asset allocation across different career stages, providing personalized strategies tailored to varying financial circumstances. Simulation results reveal that early-career workers benefit from compounding returns through steady private pension contributions, while mid-career employees enhance financial stability by transitioning from Defined Benefit (DB) to Defined Contribution (DC) plans and actively managing their assets post-retirement. This study extends the traditional MVO framework by addressing its limitations, emphasizing long-term, adaptive investment strategies designed to meet individual goals. Furthermore, the model’s potential integration with robo-advisors demonstrates its practical applicability in automated financial planning, offering scalable tools for effective pension management. By combining theoretical insights with practical applications, this study contributes to the field by empowering individuals to evaluate their retirement readiness, actively manage assets based on personal objectives, and achieve financial stability for a sustainable and confident retirement.