This review paper provides a comprehensive exploration of economic dispatch optimization strategies and problem formulations in power systems. It discusses the evolution of economic dispatch from traditional methods to modern strategies, emphasizing the increasing complexity of power systems due to the integration of renewable energy sources and other modern components. The paper categorizes commonly used techniques for solving economic dispatch problems (EDPs) into four groups: conventional mathematical approaches, uncertainty modeling methods, artificial intelligence-driven techniques, and hybrid algorithms. It identifies critical research gaps, such as the limited generalizability of findings due to the focus on single-case studies and the challenge of comparing research due to arbitrary system choices and formulation variations. The paper calls for the implementation of standardized evaluation criteria and the inclusion of a diverse range of case studies to enhance the practicality of optimization techniques in the field. The paper outlines the research methodology, which involves a systematic literature review using PRISMA guidelines. It discusses the evolution of economic dispatch paradigms, tracing the shift from conventional techniques to modern strategies in power system management. Sections 4 and 5 classify the EDP formulation and optimization techniques, respectively, covering single and multi-objective optimization approaches. Section 6 details the findings, while Section 7 summarizes the conclusions of the paper. The paper highlights the importance of economic dispatch in ensuring the reliable and efficient operation of power systems, as well as its role in minimizing operational costs and reducing emissions. It discusses the challenges posed by the integration of renewable energy sources and the need for advanced optimization techniques to address these challenges. The paper also explores various optimization techniques, including conventional mathematical methods, uncertainty modeling methods, artificial intelligence-driven techniques, and hybrid algorithms, and evaluates their effectiveness in solving EDPs. The review emphasizes the need for further research to address the limitations of existing methods and to develop more robust and flexible solutions for modern power systems.This review paper provides a comprehensive exploration of economic dispatch optimization strategies and problem formulations in power systems. It discusses the evolution of economic dispatch from traditional methods to modern strategies, emphasizing the increasing complexity of power systems due to the integration of renewable energy sources and other modern components. The paper categorizes commonly used techniques for solving economic dispatch problems (EDPs) into four groups: conventional mathematical approaches, uncertainty modeling methods, artificial intelligence-driven techniques, and hybrid algorithms. It identifies critical research gaps, such as the limited generalizability of findings due to the focus on single-case studies and the challenge of comparing research due to arbitrary system choices and formulation variations. The paper calls for the implementation of standardized evaluation criteria and the inclusion of a diverse range of case studies to enhance the practicality of optimization techniques in the field. The paper outlines the research methodology, which involves a systematic literature review using PRISMA guidelines. It discusses the evolution of economic dispatch paradigms, tracing the shift from conventional techniques to modern strategies in power system management. Sections 4 and 5 classify the EDP formulation and optimization techniques, respectively, covering single and multi-objective optimization approaches. Section 6 details the findings, while Section 7 summarizes the conclusions of the paper. The paper highlights the importance of economic dispatch in ensuring the reliable and efficient operation of power systems, as well as its role in minimizing operational costs and reducing emissions. It discusses the challenges posed by the integration of renewable energy sources and the need for advanced optimization techniques to address these challenges. The paper also explores various optimization techniques, including conventional mathematical methods, uncertainty modeling methods, artificial intelligence-driven techniques, and hybrid algorithms, and evaluates their effectiveness in solving EDPs. The review emphasizes the need for further research to address the limitations of existing methods and to develop more robust and flexible solutions for modern power systems.