<p>In the renewable energy technology industry such as wind and solar power, artificial intelligence (AI) technology is rejuvenating by implementing accurate prediction, automatic control, and predictive maintenance of various types of energy technologies. It is crucial to improve the reliability and efficiency of solar, wind, hydropower, and geothermal plants. In this paper, a recent progress of AI applications on solar, wind, hydropower, geothermal, and biomass systems integration in renewable energy sector is surveyed. The novelty is to integrate the AI forecasting, management and hybrid modeling approaches into a unified framework, which is practically valuable for smart grid and green energy policy. Energy prediction accuracy is also improved using ML and DL methods. And, hybrid models mixing AI with physical systems can boost performance and slash operational costs. Such models are, particularly, applicable in predictive maintenance since they shorten the time equipment off line and extend the life of renewable energy devices, such as solar panels and wind turbines. AI in renewable energy has a few roadblocks: issues with data quality and demand for heavy computing (as well as interpretability in AI-guided decisions). Environmental considerations also need to be included, including automation-driven job loss and bias in AI predictions. The future of that progress also brings improved energy distribution and security, and modernized energy trading if harmonized with technologies such as IoT, and blockchain. Robust legislative parameters and the ability to build AI algorithms would be very helpful in addressing these issues and helping us move toward a sustainable low-carbon energy future. Finally, a systematic responsible AI integration framework is presented in the conclusion of this study that explains the model, optimizes data-energy together and harmonizes policies.</p>