Achieving the 2016 Paris agreement goal of limiting global warming below 2 degrees C and securing a sustainable energy future require materials innovations in renewable energy technologies. While the window of opportunity is closing, meeting these goals necessitates deploying new research concepts and strategies to accelerate materials discovery by an order of magnitude. Recent advancements in machine learning have provided the science and engineering community with a flexible and rapid prediction framework, showing a tremendous potential impact. Here we summarize the recent progress in machine learning approaches for developing renewable energy materials. We demonstrate applications of machine learning methods for theoretical approaches in key renewable energy technologies including catalysis, batteries, solar cells, and crystal discovery. We also analyze notable applications resulting in significant real discoveries and discuss critical gaps to further accelerate materials discovery.