Abstract

The growing demand for energy in urban environments, coupled with the urgent need to reduce carbon emissions, necessitates innovative approaches to power generation, distribution, and consumption. Artificial Intelligence (AI)-driven smart grids offer a transformative solution by optimizing energy efficiency, integrating renewable resources, and ensuring grid stability. This paper explores how machine learning and IoT-enabled predictive analytics can enhance smart grid performance in urban areas. By addressing challenges such as demand forecasting, load balancing, and renewable energy intermittency, this study demonstrates the potential of AI to revolutionize sustainable energy management. Experimental results highlight improvements in grid reliability, cost reduction, and carbon footprint minimization, paving the way for resilient and eco-friendly urban energy systems.