Simulation-Optimization (SO) has become a reliable framework to optimize complex systems via stochastic simulation. However, recent SO methods face the significant challenges in balancing optimality and efficiency due to expensive evaluation in handling output noise. This paper proposes the SO with Adaptive Model Exchange (SO-AME) where metaheuristics dynamically switches between rigorous simulation model and fast-to-evaluate Machine Learning (ML) model based on prediction performance. The intention is to perform a vast majority of its exploration and exploitation using cheap ML, reserving expensive simulation runs only for high-performing candidates. Methodologically, ML model learns the complex input-output relation of simulation adaptively within Simheuristics loop. A case study is given to design pharmaceutical production. Compared with other robust SO methods, results validate that SO-AME significantly reduces the computational burden yet improves the optimality, offering a scalable AI-driven solution for optimizing complex systems under uncertainty.