This paper explores the application of the African Vulture Optimization Algorithm (AVOA) to solve the Economic-Emission Load Dispatch (EELD) problem in a microgrid integrating diesel, wind, and solar energy sources. The EELD problem aims to minimize both the cost and emissions of power generation while satisfying various practical constraints. AVOA, inspired by the foraging and navigation patterns of vultures, is known for its efficient exploration and exploitation capabilities. The effectiveness of AVOA is first validated using three standard test systems (10, 6, and 40 units) with and without transmission losses. The results are compared with other popular optimization techniques, demonstrating AVOA's superior performance. The impact of individual renewable energy sources on the microgrid's cost and emissions is analyzed across three distinct generation scenarios. Statistical tests, including ANOVA, Wilcoxon, and TukeyHSD, are used to assess the statistical significance of AVOA's performance. The results show that AVOA reduces the combined cost by 5.25% and emissions by 33.09% compared to the closest competitive method, making it the most effective solution for EELD in microgrids with all sources.This paper explores the application of the African Vulture Optimization Algorithm (AVOA) to solve the Economic-Emission Load Dispatch (EELD) problem in a microgrid integrating diesel, wind, and solar energy sources. The EELD problem aims to minimize both the cost and emissions of power generation while satisfying various practical constraints. AVOA, inspired by the foraging and navigation patterns of vultures, is known for its efficient exploration and exploitation capabilities. The effectiveness of AVOA is first validated using three standard test systems (10, 6, and 40 units) with and without transmission losses. The results are compared with other popular optimization techniques, demonstrating AVOA's superior performance. The impact of individual renewable energy sources on the microgrid's cost and emissions is analyzed across three distinct generation scenarios. Statistical tests, including ANOVA, Wilcoxon, and TukeyHSD, are used to assess the statistical significance of AVOA's performance. The results show that AVOA reduces the combined cost by 5.25% and emissions by 33.09% compared to the closest competitive method, making it the most effective solution for EELD in microgrids with all sources.