Abstract: As one of the most common malignant tumors of the urinary system, renal cell carcinoma (RCC) requires a precise diagnosis to guide clinical decision-making and improve prognosis. Recent advances in artificial intelligence (AI) have transformed radiological and pathological approaches to RCC evaluation. In imaging, deep learning algorithms can automatically detect and localize tumors, assisting with complex tasks such as benign-malignant differentiation, histologic subtype prediction, tumor staging, and assessment of invasiveness based on computed tomography (CT) and magnetic resonance imaging (MRI) features. In pathology, AI systems demonstrate performance comparable to or exceeding that of pathologists in tumor classification, nuclear grading, and prognosis prediction by analyzing whole-slide images. These systems can also identify molecular characteristics and prognostic markers that are difficult to detect using conventional methods. However, despite these advances, major challenges remain, including multicenter data heterogeneity, limited algorithm interpretability, and insufficient clinical validation. Future work should focus on the development of standardized, multicenter RCC databases; the creation of multimodal intelligent diagnostic systems that integrate radiomics, pathomics, and genomics; and prospective clinical validation to establish the utility of these approaches. Such efforts will support progress towards more precise, personalized, and intelligent RCC diagnosis.