To explore the full potential of predictive maintenance and energy forecasting in renewable energy sectors, extensive data collection sets, obtained through various monitoring methods, are often used to detect the early signs of system outages and failures as well as to analyze and obtain parameters such as energy output, efficiency and overall system health. As high forecasting accuracy is mandatory in these systems, a large amount of data must be utilized and extracted in the form of unseen relations and features, which can be accomplished by using artificial intelligence (AI) frameworks, libraries, and algorithms that are beginning to penetrate all areas of industry and engineering. With the rapid development in AI and broad tools provided by machine learning (ML) algorithms, it is possible to obtain solutions to significant data set problems that are very difficult to solve by classical methods. One of the most powerful techniques to solve these problems is based on the genetic algorithm (GA) approach, which mimics the process of evolution and natural selection. This review chapter shows how this evolutionary-based approach can be successfully used in various renewable energy systems to optimize performance, forecast system failures and downtimes, and plan maintenance schedules.