World transformation has meaningfully abridged the chief gas, diesel, and coal power sources. Therefore, substitute power sources based on renewable energy mainly focus on fulfilling the energy demand of the world and avoiding global warming. Among different energy sources, solar energy is the critical substitute energy source for producing electricity using photovoltaic (PV). Conversely, energy engendering performance is highly dependent on cyclical and environmental factors. The changeable version of the environment shakes energy productivity and tends to create a disapproving influence on constancy, dependability, and the grid process. Therefore, a precise prediction of PV productivity is critically required to guarantee the endurance and reliability of the grid. The detailed study reviews the perilous techniques based on PV forecast using machine learning techniques. This paper summarizes different types of renewable energy with their merits and demerits. This paper also demonstrates the key challenges existing in renewable energy using machine learning and various artificial intelligence applications in real-life scenarios. Finally, an inclusive analysis of machine learning techniques in renewable energy through a detailed literature survey is presented to forecast energy production shortly better.