With a drive to meet the growing energy demands and to curb the increasing global greenhouse emissions, clean energy is being ubiquitously harnessed. Among these, the solar technology seems to be the pinnacle in the offing. Several research groups strive hard to attain higher efficiency in solar technology. However, one of the major causes for the drastic reduction in efficiency of the solar panels is due to the effect of soiling. Soiling takes into consideration several factors like the settling of sand particles on the solar cells, accumulation of dust coverings and bird droppings. The efficiency of Photovoltaic (PV) cells is not only limited by soiling, but also by the bird droppings on the panels and layering of the snow, which is a significant cause that has a major impact on the efficiency. Thus, to maintain efficiency throughout, the PV panels have to be cleaned and monitored regularly. Many times, the cleaning agent or the water settles behind as a layer on the panel, which again yields to soiling. Thus, there is a need to restrain the dust particles (soiling) from being settled as a layer on the panels. This paper deals with the techniques using Artificial Intelligence and Computer Vision to prevent the soiling, thereby increasing the efficiency of the solar technologies. A drone affixed with an overhead camera to monitor the panels and an end effector to carry out the cleansing process is employed. The image captured is then processed by the Machine Vision techniques to assess the type of the soiling, thereby urging the drone for removing the soiled particles. Moreover, with the addition of Artificial Intelligence and Computer Vision, we are also able to draw useful insights on the impact caused by different factors that influence soiling and these predictions would be beneficial for us in taking preventive and corrective measures, which would be an advantage in the long run.