Solar panels are an increasingly popular and sustainable means of generating electricity. However, the efficiency and longevity of solar panels may get compromised by various types of faults, including diode hotspots, dust/shadow hotspots, multicell hotspots, PID hotspots, and single cell hotspots. Detecting these faults accurately is vital for maintaining optimal efficiency. Many existing methods for fault identification fall short due to inadequate feature representation and segmentation techniques. To address these limitations, an innovative approach is proposed involving entropy-based saliency map segmentation and multidomain feature analysis model for fault detection and classification in solar panels. Proposed saliency map segmentation method extracts the most relevant regions in solar panel images, improving fault detection. The entropy-driven saliency maps fault detection technique surpasses alternative approaches such as color thresholding and channel-based thresholding for fault detection in solar panels. A comprehensive set of feature representation models, including Fourier, Wavelet, DCT, Convolutional, and Gabor transformations is employed. To further enhance the precision and effectiveness of fault identification, we incorporate an Extra Trees feature selection mechanism. Classification is done with an ensemble of classification models, including k-Nearest Neighbors (kNN), Deep Forest, Support Vector Machines (SVM), Logical Regression, and Artificial Neural Networks (ANN). Empirical evaluation of the proposed model demonstrates exceptional performance, achieving F1 score of 94% for fault classification compared to existing machine learning models. Proposed multidomain analysis model gave an accuracy of 96.9% and recall of 93.5% in fault identification. Additionally, the proposed model exhibits computational efficiency, making it suitable for real-time fault detection scenarios.