At present, renewable energy resources like solar and wind attracted abundant attention, and thanks to their green, clean, inexhaustible, and recycled nature and them being free from carbon emission, renewable energy resources are the foremost promising alternative to fossil fuels. Though renewable energy resources are accessible freely, their higher upfront price, environmental dependency, and lower efficiency act as a barrier to wider implementation. The demerits of renewable energy resources are volatility, intermittence, and uncertainty which affect the stability and reliability of large-scale renewable integration into the power generation. Hence, researchers are exploring possibilities to boost accessibility and efficiency with the help of technology such as machine learning. Deep learning, a promising kind of machine learning technique, can be incorporated with renewable energy, especially solar photovoltaic (PV) systems, in three major categories, such as forecasting, accessibility, and efficiency. Boosting the efficiency of a solar PV system requires maximum power point tracking (MPPT), which maximizes the extraction of available maximum power from PV modules. As the conventional MPPT algorithms have no prior knowledge of the maximum power point (MPP) at the beginning of the perturbation, these MPPTs demand a long convergence time to achieve MPP. The need for prior knowledge of MPP is necessary to start any conventional MPP algorithm, which the 122deep learning based long short-term memory (LSTM) network provides in this work. The goal of this book chapter is to implement deep learning in the solar PV system forecasting maximum voltage to provide reference value to its MPPT technique. The case study is also presented with a conclusion.