RCC, or renal cell carcinoma, is a major worldwide health issue, accounting for approximately 2–3% of cancer diagnoses and fatalities globally. Despite progress in imaging technology, histological techniques, particularly the Fuhrman grading system, continue to be the main method for diagnosing and prognosticating RCC. Currently, percutaneous renal mass biopsy is regarded as the fine standard for preoperative grading of clear cell RCC (ccRCC), but it comes with potential complications. Consequently, non-invasive imaging approaches have become increasingly popular for assessing ccRCC grade. Radiomics, a high-throughput extraction process that generates features that can be quantitatively measured from medical images, has the potential to enhance diagnostic, prognostic, and predictive accuracy when combined with other patient information and sophisticated bioinformatics tools. Unlike biopsies, which are constrained by geographical and temporal inconsistencies, radiomics can more effectively capture tumor heterogeneity. This research sought to create a comprehensive deep learning model that forecasts survival probability in RCC patients by combining CT scans and clinical data, addressing limitations in previous studies. A comprehensive literature review emphasizes the effectiveness of radiomics and pathological data in identifying and grading kidney cancers while also pinpointing areas for improvement. Although there is evidence supporting the safety and diagnostic utility of renal mass biopsy, its underuse may lead to unnecessary surgical interventions and poorer patient outcomes.