Cross-domain fashion retrieval is a feasible method for finding the required clothing among a large number of products. Computer vision-based retrieval algorithms have been developed by many researchers. However, the precise retrieval process still faces challenges such as corrupted query images, ambiguous patterns, and an array of attributes. In this work, a novel attention-guided cascaded network has been proposed for efficient cloth retrieval by overcoming the aforementioned challenges. Initially, the input images are pre-processed with Wavelet integrated Retinex algorithm (WRA) to enhance the query cloth image without diminishing the important information. The attention-guided cascaded network is designed with global feature descriptor (GFD) and local feature descriptor (LFD) modules for extracting the domain-enriched features. These two modules are integrated with an attention block to reduce complexity and improve performance using selected features. In the LFD module, the Clothing Parsing Encoder-Decoder network (CPED-Net) is used to separate the bottom and top regions and sends them into the respective network for fine-grained feature analysis. Finally, global and local features from the modules are combined in similarity analysis for cloth retrieval. The proposed model achieves an overall accuracy of 98.54% based on the fashion retrieval benchmarks dataset. From the experimental results, the proposed model is superior to the state-of-the-art models.