Nephrolithiasis (kidney stone) is a disease which affects 7% of females and 11% of males at some stage in their life. Early identification of Nephrolithiasis is necessary to avoid complications. Imaging techniques form the basis for the detection of kidney stones and aid in locating the position, size, and the number of stones present in the renal structure. This paper reports an extensive analysis of recent trends in the detection of Nephrolithiasis using Imaging techniques. Since Computed Tomography (CT) and ultrasound imaging are commonly used in the medical field, analysis of both the methods is considered in this paper. The detailed study on various methodologies and algorithms that have been adopted on CT and ultrasound images in recent years in locating kidney stones, finding the exact size of the stones based on pixel count, enhancing image quality, obtaining better de-speckling, faster segmentation, and pre-processing of the renal images has been carried out. Based on the analysis, an artificial intelligence-based approach is proposed that will aid the medical practitioner for faster, accurate detection of Nephrolithiasis and a technique to reduce the exposure of radiation in Computed Tomography Imaging. Further, it is concluded that ultrasound techniques can be employed subsequently for preliminary diagnosis through CT if the medical practitioner recommends. © 2021 North Atlantic University Union NAUN. All rights reserved.