Unveiling Trends in Machine Learning for Smart Grids: A Comprehensive Bibliometric and Science Mapping Approach

The exponential growth of machine learning (ML) applications in smart grid (SG) research over the past decade has generated a vast and fragmented body of literature that lacks systematic synthesis. This study addresses that gap by presenting a comprehensive bibliometric and science mapping analysis of the ML–smart grid (MLSG) research landscape to date, drawing on 4156 peer-reviewed publications indexed in the Elsevier Scopus database from 2009 to 2025. The principal contributions of this study are fourfold. First, it provides a rigorous quantitative mapping of MLSG publication growth from one document in 2009 to 1163 publications in 2025, representing a growth rate of 116,200%, thereby establishing a definitive baseline for tracking future scholarly development in the field. Second, it identifies the key actors driving MLSG research, including the most prolific authors (Nadeem Javaid, Alsabaan M.), leading institutions (King Saud University, Tennessee Technological University), and dominant nations (India, China, United States), which offers researchers and funding bodies actionable intelligence on collaboration opportunities and research leadership. Third, through keyword co-occurrence and cluster analysis, the study maps the three dominant thematic hotspots structuring current MLSG research—Smart Grid Security, Power Load Forecasting, and Advanced Energy Management—providing a structured intellectual framework that can guide future research prioritization. Fourth, the study delivers a critical thematic literature review of these three hotspots, synthesizing the most impactful ML methodologies and applications reported across 4156 publications, including deep learning-based intrusion detection, ensemble forecasting models, and reinforcement learning-driven energy management. Collectively, these contributions offer a robust evidence base for researchers, policymakers, and industry practitioners seeking to navigate, benchmark, and advance the field of ML-enabled smart grid systems.