This research uses an advanced intuitionistic fuzzy optimization framework to study the Multi-Objective Emission and Economic Load Dispatch (MEELD) issue under ambiguous and imprecise operational conditions. Because fuel cost and pollution emissions must be minimized simultaneously while carefully adhering to power balancing calculations, generator capacity limitations, and system operational constraints, the MEELD problem is intrinsically complicated. A strong optimization method that can manage uncertainty and decision ambiguity is required because of these competing goals. In order to overcome this difficulty, a mathematical model that incorporates vagueness related to system characteristics and decision variables is developed in both fuzzy and intuitionistic fuzzy contexts. The intuitionistic fuzzy model, in contrast to other fuzzy methods, takes membership, non-membership, and hesitation degrees into account, offering a more thorough depiction of uncertainty. Using intuitionistic fuzzy aggregation operators, a structured solution approach is suggested to convert the multi-objective optimization problem into an equivalent single-objective formulation. A three-unit thermal power generation system, which is frequently used as a benchmark in MEELD research, is used to illustrate the efficacy of the suggested methodology. The intuitionistic fuzzy optimization method effectively accomplishes an ideal trade-off between economic and environmental goals, according to simulation data. When compared to conventional optimization techniques, the resulting solutions show better compromise solutions, increased flexibility, and improved convergence characteristics. In summary, the MEELD problem continues to be a crucial component of contemporary power system operation, especially when considering sustainable energy management and environmental requirements. For large-scale power system applications needing simultaneous economic and emission optimization, the suggested intuitionistic fuzzy optimization approach offers a technically sound and effective framework for decision-making.