This paper provides a comprehensive survey of AI-driven approaches for optimizing power consumption across various application domains. The authors review 17 research methods, focusing on the integration of AI techniques such as fuzzy logic control, reinforcement learning, genetic algorithms, swarm intelligence, machine learning, neural networks, and predictive analytics. These methods are applied to areas like photovoltaic systems, thermal energy management, smart grids, and industrial automation. The paper highlights the benefits of AI in improving energy efficiency, reducing costs, and enhancing sustainability. It also discusses the challenges and future directions in the integration of AI for power consumption optimization, emphasizing the need for addressing data security, technological infrastructure, and regulatory concerns. The survey concludes by outlining the potential of AI in advancing the energy sector, particularly in the context of renewable energy sources and smart grid applications.This paper provides a comprehensive survey of AI-driven approaches for optimizing power consumption across various application domains. The authors review 17 research methods, focusing on the integration of AI techniques such as fuzzy logic control, reinforcement learning, genetic algorithms, swarm intelligence, machine learning, neural networks, and predictive analytics. These methods are applied to areas like photovoltaic systems, thermal energy management, smart grids, and industrial automation. The paper highlights the benefits of AI in improving energy efficiency, reducing costs, and enhancing sustainability. It also discusses the challenges and future directions in the integration of AI for power consumption optimization, emphasizing the need for addressing data security, technological infrastructure, and regulatory concerns. The survey concludes by outlining the potential of AI in advancing the energy sector, particularly in the context of renewable energy sources and smart grid applications.