This paper presents a comprehensive computational framework integrating sequential statistical inference algorithms and metaheuristic optimization methods for multi-stage decision-making problems in complex systems. The proposed approach employs a four-module architecture combining hypothesis testing with sequential probability ratio test mechanisms, simulated annealing algorithms for combinatorial optimization, Monte Carlo simulation techniques for large-scale stochastic computing, and Bayesian inference mechanisms for dynamic probabilistic reasoning. Experimental evaluations demonstrate that the integrated framework achieves optimal decision configurations across multiple operational scenarios, with cost reduction rates exceeding 35 percent and decision accuracy improvements of approximately 90 percent compared to conventional methods. The framework exhibits robust performance in handling high-dimensional decision spaces involving thousands of binary choice variables and demonstrates scalability to real-time computational environments. Key contributions include the development of adaptive sampling strategies for statistical inference, temperature-controlled acceptance mechanisms for escaping local optima in discrete optimization landscapes, and probabilistic update protocols for handling uncertainty propagation in sequential decision chains. The proposed algorithms provide a systematic methodology applicable to intelligent decision support systems across distributed computing platforms.