Chronic kidney disease (CKD) affects over 10% of the global population, presenting significant health challenges. Early detection and personalized treatment are crucial for managing CKD and preventing complications. Traditional diagnostic methods have limitations, prompting the exploration of artificial intelligence (AI) for enhanced diagnostic precision. This systematic review examines AI’s role in interpreting Complete Blood Count (CBC), Iron, and Lipid profiles for CKD prognostication and management. AI algorithms, including machine learning and deep learning models, show improved accuracy in detecting CKD progression, predicting outcomes, and optimizing treatment strategies. The review discusses the effectiveness of various AI models, highlights challenges related to data quality and ethical considerations, and emphasizes the importance of interdisciplinary collaboration. Key findings indicate that AI can significantly enhance diagnostic precision and personalized treatment plans, offering a proactive approach to CKD care. However, issues such as data privacy, algorithmic bias, and the need for robust regulatory frameworks persist. Integrating AI into clinical practice holds promise for revolutionizing CKD management, making it more efficient, effective, and patient-centric. Ongoing research and ethical considerations are crucial to unlock AI’s full potential in CKD care.rds.