The U.S. power grid faces increasing risks from cyber-physical attacks that could disrupt essential services and compromise national security. The fusion of artificial intelligence with cybersecurity protocols is perceived to present a consequential shift in the current efforts to safeguard critical infrastructures. This manuscript aims to identify the most critical vulnerabilities within the power grid's infrastructure, develop advanced machine learning-based threat detection systems, and propose automated response mechanisms to mitigate impacts effectively. By integrating comprehensive vulnerability assessments, innovative detection technologies, and autonomous response strategies, this study seeks to enhance the resilience of the power grid against sophisticated cyber threats.