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Abstract: The study explores the identification of strongly lensed galaxies, rare astronomical phenomena where massive objects bend light from distant sources, creating magnified and distorted images. Strongly lensed galaxies are crucial in astronomy as they provide insights into dark matter distribution, galaxy mass profiles, and cosmological parameters. To efficiently identify these lenses from vast datasets, images were ranked based on their probability of being strong lens candidates. A Learning-to-Rank (LTR) approach using Support Vector Machines (SVM) was implemented and compared with Convolutional Neural Networks (CNNs) and other classifiers. LTR with SVM demonstrated superior performance, achieving a high AUC and accuracy, outperforming CNNs in both efficiency and classification precision. This method facilitates efficient candidate selection, enhancing the potential for cosmological studies.