Optimization of seismic workflow parameters is pivotal for enhancing the efficiency, accuracy, and cost-effectiveness of exploration projects. This review synthesizes the state of the art in mathematical and computational models—ranging from traditional gradient-based algorithms and genetic algorithms to machine-learning approaches and hybrid metaheuristics—applied to key seismic processing stages such as acquisition design, trace editing, velocity model building, migration parameter tuning, and imaging inversion. We critically evaluate the criteria and objective functions used to quantify data quality, signal-to-noise ratio, and computational load, highlighting how different optimization strategies address the trade-offs among resolution, runtime, and resource consumption. Common challenges—including non-convex search spaces, high-dimensional parameter domains, data heterogeneity, and integration with real-time feedback—are discussed, along with mitigation strategies such as surrogate modeling and adaptive sampling. We then showcase demonstrated benefits in case studies, including reduced turnaround times, improved imaging fidelity, and lower acquisition costs. Finally, we identify emerging trends—such as deep-reinforcement-learning frameworks, cloud-computing scalability, and digital-twin implementations—that promise to further advance seismic workflow optimization. By providing a comprehensive framework of techniques, challenges, benefits, and future directions, this review aims to guide geophysicists, data scientists, and project managers in selecting and tailoring optimization models to the specific demands of diverse exploration scenarios.