Global energy systems face unprecedented challenges due to rising energy demand, driven by factors such as industrial growth, technological advancements, and population increases. According to the International Energy Agency [1] , global energy demand is projected to increase by 25% by 2040. Currently, buildings account for approximately 30% of global energy usage, underscoring the significant impact AI-driven energy management could have, especially in sectors with high energy demand, like the commercial and residential sectors [2] . China, one of the largest energy consumers globally, has demonstrated that AI integration in its smart grid can save approximately 500 million metric tons of CO₂ emissions annually, as AI enhances energy use by optimizing distribution [3] . In response to these pressures, Europe has implemented AI-powered infrastructures that optimize renewable energy deployment, facilitating an eco-friendly urban development strategy that aligns with smart city goals and helps achieve net-zero objectives [4] . Simultaneously, the world’s reliance on renewable energy continues to grow, with renewables accounting for nearly 29% of global electricity generation in 2022, a figure expected to rise to 42% by 2028 [5] . This growing trend is visible in Figure 1 and Figure 2 , which show the world’s increasing dependency on renewable energy. While Figure 3 shows the distribution of this dependency across the globe as of 2022.

However, the fluctuating nature of renewable energy sources, such as wind and solar, presents significant challenges. Wind and solar energy, while critical for reducing carbon emissions, are intermittent and can lead to variability in supply. This variability is expected to impact energy demand consistency by 20% - 30%, leading to periods of both surplus and shortage, which destabilizes energy infrastructure and complicates grid management [1] [6] . Smart grid technologies integrated with AI are proving essential for managing this variability. By using predictive modeling, these systems can stabilize energy supply and demand fluctuations, as evidenced during the COVID-19 pandemic when energy consumption patterns shifted dramatically, and AI-based systems enabled grids to adapt effectively [7] . Similarly, the EU has responded to this by integrating AI with smart grids, which facilitates real-time data analysis and optimal distribution, reducing system strain and increasing resilience against fluctuations [4] .

Traditional energy grids, which were designed for centralized and consistent power generation, struggle to accommodate these fluctuations. These grids primarily rely on manual processes and limited automation, which are no longer sufficient to manage the complexities of modern energy systems [13] . AI’s predictive analytics capability significantly improves energy infrastructure stability by optimizing real-time distribution, reducing unplanned downtime by up to 40% [14] [15] .

Compared to earlier research that focused primarily on traditional grid systems, this study emphasizes the transformative potential of AI in optimizing modern energy systems. Prior works, such as the integration of basic fault detection mechanisms in isolated grids [16] , lacked robust cybersecurity measures. This study stresses need to incorporate advanced cryptographic frameworks and AI-enabled fault detection mechanisms to ensure both operational reliability and security.

Despite its promise, AI integration in energy infrastructure remains in its early stages, facing challenges such as high costs, data availability issues, and cybersecurity concerns, which limit its widespread adoption, particularly in developing countries with insufficient infrastructure and resources [7] [17] . Table 1 outlines the timeline and objectives of AI strategies across different nations, showcasing how governments globally are leveraging AI to transform energy systems.