AI-driven technologies are using embedded evolutionary algorithms, fuzzy and neural logics to extract maximum solar energy. It is the need of an hour to optimize the Solar PV performance variables like aligning with the sun’s trajectory in intelligent tracking mechanisms with an addition of regular cleaning and substantial cooling of panels simultaneously. The chapter headings have aimed to give insight of the inter dependency amongst these Solar PV performance variables in the context of multi-objective optimization. By implementation of these intelligent algorithms, it is concluded later in the chapter that it has significantly reduced the levelized cost of energy (LCOE) by minimizing the need for unnecessary panel replacements and by controlling variables like temperature and contamination ratios. Furthermore, it is also shown that by optimization, solar PV electrical and thermal efficiencies have increased and hence solar energy is being declared as a sustainable alternative.