Economic Dispatch (ED) is a vital optimization problem in power systems, focused on minimizing generation cost while satisfying operational and environmental constraints. The increasing integration of renewable energy sources such as wind, solar, and hydro has made ED more complex due to intermittency, variability, and forecasting challenges, necessitating advanced optimization frameworks beyond conventional mathematical programming. This review analyzes ED approaches under varying load and renewable integration conditions, drawing on peer-reviewed literature from IEEE Xplore, ScienceDirect, Springer, Elsevier, and Scopus (2013–2025). Classical methods like lambda iteration and quadratic programming remain effective for small-scale convex ED problems but fail to address nonlinearities and renewable-related uncertainties. Meta-heuristic algorithms, including Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Differential Evolution (DE), effectively solve nonconvex problems but face limitations in convergence speed and scalability. Hybrid frameworks, particularly those incorporating artificial intelligence and fuzzy logic, offer balanced trade-offs between solution quality, robustness, and computational efficiency. Stochastic and robust optimization methods provide structured ways to manage uncertainty, though their high computational demands hinder widespread application in large-scale systems. Overall, no single ED method universally outperforms others; the choice depends on system size, renewable penetration, and operational objectives. Key research gaps include the scalability limits of meta-heuristics, limited exploration of AI-driven hybrid models, insufficient large-scale real-world validation, and inadequate emphasis on environmentally constrained ED. Future work should focus on adaptive, emission-conscious, and smart grid-integrated approaches that enhance efficiency, sustainability, and resilience in renewable-rich power systems.