This paper reviews and synthesizes key mathematical modeling approaches applied to strategic manpower planning, including deterministic and stochastic models, Markov and semi-Markov processes, linear and integer programming, system dynamics, queuing theory, and simulation-based models. The models address essential manpower planning issues such as recruitment, promotion, attrition, training, succession planning, and skill-mix optimization across multiple time horizons. Emphasis is placed on balancing cost efficiency, service levels, and workforce sustainability while accounting for uncertainties in demand, employee behavior, and policy constraints. The study highlights the strengths and limitations of each modeling approach and discusses their applicability in large, hierarchical organizations. The paper concludes by outlining future research directions, including the integration of artificial intelligence, big data analytics, and hybrid modeling frameworks to enhance the robustness and adaptability of strategic manpower planning systems.