Solar energy harvesting through photovoltaic (PV) modules is often hindered by dust accumulation, which reduces efficiency. This study uses Image Processing and Artificial Intelligence (AI) for condition monitoring and maintenance of solar panels, specifically to assess the impact of dust on power generation. By leveraging machine learning and deep learning, which are branches of AI, power loss was predicted based on images, which can serve as a deciding parameter for the cleaning method and frequency of solar PV modules. Using the ‘PV-Net’ dataset, which includes panel images, irradiance, and power loss values, features such as Energy and Homogeneity from the Gray-Level Co-occurrence Matrix (GLCM), Earth Mover’s Distance (EMD) and Hellinger distances with a reference clean panel image were extracted. These, along with irradiance values, served as inputs for regression models developed with MATLAB's Regression Learner app. Additionally, a convolutional neural network (CNN) was used for automatic feature extraction and regression. Both models were trained including and excluding the irradiance as an input feature and their performance was compared. This study demonstrates the potential of computer vision and AI for efficient solar PV module maintenance.