This The growing incorporation of renewable energy sources and the implementation of intelligent grid systems have prompted the need for inventive demand response strategies. These approaches aim to optimize energy consumption and bolster grid stability . This study explores intelligent demand response techniques for peak load shaving in smart grids. Harnessing sophisticated communication and control technologies without plagiarism., the proposed strategies aim to dynamically adjust electricity consumption patterns among end-users to mitigate peak demand, thereby optimizing grid performance. The research introduces a framework for intelligent demand response, incorporating machine learning algorithms to predict and manage peak load periods. By analyzing historical consumption patterns, weather forecasts, and user preferences, the system autonomously adapts to changing conditions, providing real-time insights to both consumers and grid operators. The efficiency of the suggested approaches is assessed using simulations and case studies, ensuring originality and avoiding plagiarism., demonstrating significant reductions in peak demand and associated benefits such as cost savings and enhanced grid reliability..