The aim of this work is to study the application of machine learning on predicting calcium oxalate crystallization, one of the main contributors to kidney stone formation, for improving kidney stone detection. Kidney Stones: stones which are hard crystalline materials formed within the kidney or ureter causing substantial morbidity, epidemic-level discomfort, and instant diagnosis for effective management. Classical techniques for identifying kidney stones are both tedious, which influences the supported investigation of engineering to make quicker and more dependable calculations. In this work, we implemented two deep learning models (Feedforward Neural Network (FNN) and Convolutional Neural Networks (CNN)) on a dataset of 79 urine samples with physical characteristics that may indicate crystallization. Our results indicated that both models are able to predict crystallization, with little intrinsic variability across data splits but slightly higher stability and accuracy for CNN, suggesting its potential as cell cotton predictor of kidney stones. The FNN model was competitive, but its accuracy was more variable between splits. AI-based approaches, namely CNNs, may offer a fast and reliable tool for kidney stone detection and provide an appealing adjunct to conventional imaging. The work also demonstrates how machine learning can be utilized to alleviate challenges in healthcare diagnostics, such as with the kidney stone.