eng Oriental Scientific Publishing Company Biomedical and Pharmacology Journal 0974-6242 2025-02-20 18 March Spl Edition 45 58 10.13005/bpj/3072 64197 article Shivani Verma 1 Pawan Kumar Singh 1 Gagandeep Kaur 2 Anannya Vashistha 2 Shreya Pansari 2 Department of Electronics and Communication Engineering, SRM University, Sonipat, India Department of Electronics and Communication Engineering., Guru Tegh Bahadur Institute of Technology Delhi India This study investigates the use of machine learning (ML) methods for detecting kidney stones, a field that has gained increasing attention due to limitations in traditional diagnostic methods such as ultrasound and Computed Tomography (CT) scans. The aim of this review is to evaluate different machine learning (ML) algorithms employed to improve the accuracy and efficiency of kidney stone detection, with an emphasis on supervised, unsupervised, and reinforcement learning approaches. Key findings suggest that ML techniques namely Support Vector Machines (SVM), Random Forests (RF), and Deep Learning (DL) algorithms, including the VGG16 (Visual Geometry Group) Convolutional Neural Network (CNN) model, have significantly improved diagnostic accuracy. In particular, VGG16 has demonstrated promising results in feature extraction and classification tasks within medical imaging. Furthermore, this study examines challenges related to data accessibility, model transparency, and clinical integration, along with potential advancements in hybrid models and personalized medicine. https://biomedpharmajournal.org/vol18marchspledition/non-invasive-kidney-stone-prediction-using-machine-learning-an-extensive-review/ Convolutional Neural Network Kidney Stone Detection Machine Learning Random Forests Support Vector Machines VGG16 Model