Due to radiologists' subjective interpretation, radiomics texture analysis offers objective image information that would not otherwise be available. In this paper I examined the implications of radiomics for the evaluation of kidney tumors and produced a thorough research. I looked up English-language research on radiomics applications in renal tumor assessment in the PubMed-MEDLINE database till 2023. 52 papers total are included in the analysis, which is divided into four categories: patient outcome prediction, nuclear grade prediction, renal mass differentiation, and gene expression-based molecular markers. The research's main goal is to accurately differentiate benign from malignant renal masses, with a particular emphasis on renal cell carcinoma (RCC) subtypes, angiomyolipoma without visible fat, and oncocytomas. Nuclear grade prediction may help in more appropriate patient selection for risk-stratified treatment. Individualized treatment regimens may be made easier by predicting patient responses and outcomes from targeted drugs, and gene mutations predicted by radiomics may serve as stand-in biomarkers for high-risk disorders. Studies have generally demonstrated that radiomics is better than expert radiological interpretation. Compared to subjective visual interpretation, radiomics provides a more objective way to diagnose kidney cancers. Adding more clinical and imaging data to radiomics algorithms will improve tailored therapy and boost the accuracy of tumor prediction.