This thesis presented a thorough investigation into developing a machine learning model for the classification of Renal Carcinoma Cancer in medical images. The primary aim was to create an accurate and efficient classifier capable of differentiating between malignant and non-cancerous images, thus contributing to earlier cancer detection and improved patient outcomes. The research commenced with an extensive literature review and selection of five renal cancer-related datasets. The chosen dataset was preprocessed using techniques such as outlier removal, denoising, edge detection, histogram equalization, and data augmentation. Several machine learning models, including ANN, SVM, CNN's VGG16, and Transformer's Swin Transformer, were assessed. The SVM model achieved the highest level of accuracy and precision, while the Transformer model exhibited the best recall. The user-friendly website provided instant feedback on whether the uploaded image was malignant or noncancerous.