In the digital era, low-cost hardware like sensors and cameras has led to the creation of numerous image databases for various applications. This has led to the need for retrieval systems that rely on visual content, and these types of systems are called content-based image retrieval (CBIR) systems. It’s a method utilized to locate and extract digital images from extensive databases by considering their visual attributes, as opposed to relying exclusively on metadata or written descriptions. In order to obtain appropriate images from the database, features including colour histograms, texture patterns, and shape descriptors are being used to determine similarities between the images. Over the course of the last twenty years, efforts have been directed towards creating hand-crafted features tailored for CBIR systems. However, depending solely on distance-based retrieval methods is a formidable task. Hence, this study strives to leverage the capabilities of classifiers as well for the purpose of retrieval. So, the proposed CBIR paradigm uses not only the hand-crafted features but also the strength of the classifier with weighted distance metricTherefore, the proposed CBIR paradigm is designed in a way that it uses the strength of the NaiveBayes classifier to compute weighted distance using hand-crafted wavelet features to get similar images from the database. The performance of the proposed method is evaluated on three most popular texture datasets and found to be better among all the methods reported in this work.