Artificial intelligence (AI) is a rapidly advancing tool in healthcare, which might have significant implications in nephrology. Integrating AI, particularly through models like GPT-3 and GPT-4, has potential in medical education and diagnostics, achieving accuracy in clinical assessments. AI's ability to analyze large, complex datasets from diverse modalities (electronic health records, imaging, and genetic data) might enable early detection, personalized treatment planning, and clinical decision-making. Key developments include AI-driven chronic kidney disease and acute kidney injury predictive models, which utilize machine learning algorithms to predict risk factors and disease onset, thereby allowing timely intervention. AI is enhancing non-invasive diagnostics like retinal imaging to detect kidney disease biomarkers, offering a promising and cost-effective approach to early disease detection. Despite these advancements, AI implementation in clinical practice faces challenges, including the need for robust data integration, model generalizability across diverse patient populations, and ethical and regulatory standards adherence. Maintaining transparency, explainability, and patient trust is crucial for AI's successful deployment in nephrology. This article explores AI's role in kidney care, covering its diagnostic applications, outcome prediction, and treatment, with references to recent studies that highlight its potential and current limitations.