Optimized Hybrid Machine Learning Approaches in Empowering Government Initiatives: Trends and Challenges

Abstract

Machine Learning (ML) is rapidly transforming how governments make decisions, allowing them to base policies on solid evidence, use resources more efficiently, and deliver public services more effectively. Its applications are wide-ranging—helping to predict public health trends, detect fraud in welfare programs, and improve disaster preparedness. Yet, despite these advances, traditional ML models that rely on a single algorithm often fall short when it comes to accuracy, clarity, scalability, and speed. A promising way forward is the use of optimized hybrid ML approaches, which blend multiple algorithms with sophisticated data filtering and fine-tuned parameters to overcome these limitations.

This review brings together research from around the world on ML in governance, focusing on hybrid models, how they are built, and how they address today’s challenges. Drawing on international case studies, it examines emerging trends, pinpoints barriers to implementation, and offers a practical framework for adopting hybrid ML in public administration. The analysis also highlights important gaps in current research, including a lack of cross-disciplinary collaboration, the absence of governance-specific responsible AI guidelines, and limited real-world testing of hybrid models. The paper closes with recommendations for future work—emphasizing the need for explainable hybrid designs, ethically guided deployment, and long-term pilot projects to measure real-world impact.