The Anshan-Benxi area is an important iron ore resource base in China, which is renowned for hosting numerous sedimentary-metamorphic iron deposits. Unlike typical hydrothermal deposits, this type of ore deposits lacks distinct hydrothermal alteration mineral indicators. Coupled with high vegetation coverage and intensive human activities in the area, the extraction of remote sensing information is particularly challenging. Consequently, except for few typical open-pit deposits, the application of remote sensing technology in iron metallogenic prediction in this region has been relatively limited. This work utilized multi-source satellite images including Landsat-9, Sentinel-2A, GF-2, and ASTER 30 m resolution elevation data, and extracted lithological, mineralogical, and structural information related to iron mineralization. Results show that standard principal component analysis combined with histogram stretching can effectively enhance the contrast of different lithologies in Landsat-9 multispectral imagery, which aids to facilitate the characterization of their spatial distribution. Ferrous and hydroxyl mineral anomalies extracted from Landsat-9 data using band ratio and principal component analysis methods not only indicate known iron deposits but can also help identify greenest-facies metasedimentary rocks of the Yingtaoyuan Formation in the Anshan Group. The optimal band combination and directional filtering techniques were employed to enhance the textural features of Sentinel-2A imagery. Combined with topographic indices derived from ASTER elevation data, the fault structures within the area can be effectively identified. After removing false structures and anomalous interference using GF-2 high-resolution remote sensing data, and by integrating geological, mineral resource, and aeromagnetic datasets, four prospective mineralization areas were delineated. This work not only provides a reference for subsequent iron ore exploration in the area but also proposes a multi-source remote sensing and information fusion approach that can offer valuable insights for remote sensing prospecting in similar terrains.