Hybrid metaheuristic frameworks for multi-objective optimization show promise in addressing ethical decision-making in digital governance, though direct integration of ethical components remains limited, with only one study explicitly incorporating human-centered sustainability and fairness in Industry 5.0 contexts (Bezoui et al., 2024). Across the evidence, these frameworks effectively balance conflicting objectives such as cost minimization, performance maximization, and resource efficiency, generating diverse Pareto fronts that could support trade-offs in governance scenarios like policy formulation and AI systems; for instance, evaluations demonstrate superior convergence and diversity compared to standalone methods like NSGA-II, with hypervolume improvements not quantified but consistently reported as enhanced in benchmark tests (Kesireddy & Medrano, 2024), (Kafafy, 2013). In cloud-based applications relevant to e-government, hybrid approaches reduce makespan and energy consumption while optimizing security risks, achieving better load balancing than traditional algorithms (Neelakantan & Yadav, 2022), (Anwar & Deng, 2018). The topic matters because digital governance increasingly relies on AI-driven decisions where ethical trade-offs—such as fairness versus efficiency—are critical, yet existing optimization tools often overlook moral dimensions, creating gaps in equitable policy implementation. Secondary findings highlight the prevalence of evolutionary and swarm-based hybrids, which excel in exploration-exploitation balance for engineering and scheduling problems adaptable to governance, but ethical metrics like bias mitigation are rarely formalized. Implications include adapting these frameworks for regulatory AI ethics in e-government, potentially enabling transparent multi-stakeholder decisions; however, gaps persist in explicit ethical modeling and real-world governance validations, necessitating targeted extensions for comprehensive ethical decision support.