Renal irregularities are serious medicinal condition that is becoming more common and killing more people each year. In its early stages, renal irregularities are curable, but it can progress irreversibly and result in renal failure. Cyst development, kidney tumours, and stone are the three predominant kidney irregularities that impair renal function among numerous diseases. The prompt detection and treatment of renal disease is a major challenge to the medical community. If renal problems like stones, cysts and tumours are not detected at an early stage, renal failure may ensue. Computer-assisted diagnostics is necessary to complement medical assessments made by clinicians and specialists as renal disease is more spread, there are less clinicians accessible, assessment and monitoring rates are rising, especially in developing nations. Though they still don’t perform well, artificial intelligence methods like machine and deep learning have been utilized in literature to identify illness. This study uses CNN model for renal disease categorization and prognosis that is based on deep learning. For exploration, we use a benchmark CT kidney dataset from Computed Tomography. CNN first preprocesses the data before extracting the features from the pictures. The suggested method successfully classifies renal illness, with a notable accuracy of 99.3%, 99.5% precision, 95.3% recall, and 9.88% F1-score.