Kidney disease affects millions of people globally and is a global health concern. Early detection and risk stratification are crucial for effective management and treatment. New developments in machine learning (ML) present feasible alternatives for renal risk assessment that are quick, precise, and non-invasive. The multidisciplinary optimization strategy to improve kidney disease detection with a machine learning algorithm is described in this abstract. The principal aim of this research is to find a strong machine learning model that can precisely forecast the likelihood of renal illness through the examination of multidimensional patient information. In order to optimize the detection process, the project intends to blend modern computational approaches, data science, and clinical knowledge. To guarantee high-quality input for the machine learning models, data preprocessing procedures comprised feature selection and handling of missing values. Feature importance was evaluated using techniques like mutual information and SHAP (SHapley Additive exPlanations) values. A number of machine learning methods were assessed, such as Random Forest, Gradient Boosting, Neural Networks, and Logistic Regression. The research effectively created an optimized machine learning model for renal disease risk assessment, demonstrating the possibilities of combining cutting-edge data science methods with clinical knowledge. These machine learning models can contribute positively to the development of digital twin technology in healthcare domain.