The intricacy, nonlinearity, and heterogeneity of kidney pathophysiology pose a number of problems for conventional approaches of diagnosis and therapy that rely on hypothetical-deductive reasoning and linear statistics. An increasing amount of research, however, indicates that kidney disease could benefit greatly from decision support systems that are powered by artificial intelligenceArtificial intelligence (AI) and are based on observed examples and patterns. These modern AI applications rival human accuracy in kidney tumor detection from imaging, identify modifiable risk factors linked to the onset and progression of chronic kidney diseaseChronic kidney disease (CKD), and improve decision-making and prognostication in transplantation of kidneys. Acute kidney injuryAcute kidney injury (AKI) can be accurately predicted well before biochemical changes become apparent. The goal of this article is to address the growing global health issue of chronic kidney diseaseChronic kidney disease (CKD), which is mostly brought on by diabetesDiabetes and high blood pressure. It emphasizes on the important elements of CKD, such as its causes, diagnostic indicators like Glomerular Filtration Rate (GFR), and the increased risk of early mortality in CKD patients. The work proposes a ground-breaking deep learningDeep learning model customized for early CKD detection and predictionPrediction, using artificial intelligenceArtificial intelligence, in recognition of the difficulties faced by medical professionals in early diagnosis. The performance of the unique deep learningDeep learning model against cutting-edge machine learningMachine learning approaches is rigorously compared as a key component of this research, which could revolutionize CKD diagnosis and enhance patient outcomes globally.