Over the past few decades, a substantial shift toward adopting diverse renewable energy sources has taken place in response to the ongoing depletion of fossil fuels, a looming threat to the planet. The swift combustion of fossil fuels, including coal and oil, contributes to the emission of greenhouse gases like carbon dioxide. This high concentration significantly exacerbates global warming, contributing to the melting of polar ice and consequent rises in sea levels. In response to the challenge of global warming, various strategies have been employed, utilizing efficient energy conversion technologies like fuel cells, improving present operations through recovering waste heat, and utilizing energy from sustainable sources. Amid the pandemic crisis, renewable energy sources have demonstrated resilience and a rise of interest. The IEA reported an impressive 8% decline in global CO2 emissions. Large-scale renewable energy projects are increasingly recognized as a pivotal strategy for combating global warming. Commercial-scale initiatives encompassing solar thermal, solar photovoltaic (PV), geothermal, and wind technologies have gained traction on a global scale. However, a significant problem is presented by the short-term nature of alternative power sources, especially solar and wind. Artificial intelligence (AI), a concept that endeavors to replicate humanlike intelligence in machines, holds significant transformative potential. AI encompasses problem-solving, learning, decision-making, and validation, resembling human capabilities. This chapter reviews the noteworthy advantages of AI in the production and utilization of clean energy, which include error reduction, rapid decision-making, and digital support. The chapter also gives an overview on the utilization of subsets like machine learning and deep learning in enabling machines to independently enhance accuracy through data input and output. In the energy sector, AI offers solutions to a spectrum of energy-related challenges. These encompass modelling and optimizing energy systems, forecasting energy production and consumption, optimizing overall system efficiency, and managing energy distribution. Integration of AI with smart energy systems catalyzes innovation, bridging the gap between physical objects and system representations. AI techniques are deployed in energy systems design, energy efficiency monitoring, energy generation forecasting, and energy storage optimization.