In the Western world, kidney stone disease is quite common. Small kidney stones often pass quickly. In many cases, patients in this category don't need any more care. However, certain nephrolithiasis patients develop very large stones that, if left untreated, may lead to severe complications and chronic disorientation. Our suggested wavelet technique avoids log and noteworthy change, accounting for the fully developed dot as additional material signal-subordinate turmoil with zero mean, which is a counterargument to the idea that intensive therapy and countermeasures can eradicate the disease entirely. In order to consolidate data from multiple recurrence groups, measure local consistency among image components, and enhance image quality via watershed calculation, the proposed wavelet transformation strategy is characterized by a Neural network. Therefore, we'd want to learn more about its picture-handling capabilities. The next step in identifying kidney stones is to locate the cycle that does it. This is why we blindly adhere to the prevailing ideas of our day. Our venture's first steps are these. Take use of the completed data set by taking Computed Tomography (CT) scans of the kidney for use in creating informative photographs. Because of these methods, which involve spotting the ailment and its stages. Consuming wasteful foods, like as tomatoes on a regular basis, may lead to renal problems and increase the likelihood of developing kidney stones. To prevent this, we will use pre-processing, partitioning, highlight extraction of GLCM, and brain network grouping calculations with a specific place in mind right from the start.