Simulation-Based Optimization (SBO), as a hybrid approach combining simulation modeling and optimization techniques, is a powerful tool for solving complex decision-making problems that cannot, or cannot be reliably, solved by classical methods due to uncertainty, nonlinear and discrete behaviors, high dimensions, and the black-box nature of systems. The combination of simulation's descriptive power and optimization's prescriptive power enables accurate analysis of dynamic, uncertain environments and the identification of optimal or near-optimal decision-making policies. This article provides a comprehensive overview of the fundamental concepts, classification of approaches, and key methods in the field of SBO. In this regard, a variety of optimization methods used alongside simulation—including deterministic and stochastic methods, metaheuristics, machine learning-based approaches, multiobjective frameworks, and constrained optimization techniques—are reviewed. Special attention is paid to derivative-free methods and surrogates, which are common for optimizing expensive, noisy, and non-differentiable models. The role of various simulation approaches, such as discrete-event, continuous-time, agent-based, and Monte Carlo simulations, in shaping the SBO landscape is also discussed. In the applications section, the paper reviews key areas including Supply Chain Management (SCM), healthcare systems, transportation and logistics, energy and environment, and military and defense applications. For each area, it is shown how SBO can improve strategic and operational decision-making under uncertainty, enhance system performance, and increase its resilience. In addition, the significant challenges of SBO, including high computational cost, model uncertainty, data limitations, and high dimensionality, are analyzed. Finally, the article highlights emerging trends, including the integration of machine learning and simulation, the development of digital twins, the use of high-performance computing, and the move towards real-time optimization. Overall, this review aims to provide a comprehensive overview of the theoretical foundations, methodological advances, practical applications, and future research directions in SBO.