Frailty is a growing public health concern among older adults with diabetes, linked to increased risks of mortality, disability, and hospitalization. China currently has the largest population of older adults with diabetes, a number that is expected to more than double by 2045, underscoring the urgent need for early frailty risk identification. Existing traditional predictive tools often lack the accuracy and practical application for early identification and intervention, prompting interest in machine learning approaches. This study aims to develop and validate a machine learning based prediction model and an interpretable electronic risk calculator tailored to older adults with diabetes using nationally representative data from China, with the research scheduled to run from July 2025 to May 2026. This study distinguishes itself by offering a more robust evidence base compared to traditional methods, and leveraging the high quality and nationally representative CHARLS (China Health and Retirement Longitudinal Study) dataset. Led by Peking University from 2011 to 2020, in which 17,000 adults aged 45 and above were interviewed, the CHARLS survey dataset contains the individual and family information of these adults, including physical and mental health status and depressive symptoms, chronic disease status and self-rated health, and demographic variables such as gender, age, marital status, and education level. The Fried frailty phenotype will be used to classify frailty status of these participants and select predictors based on a prior systematic review and expert consultation. A range of eight machine learning algorithms are proposed to be tested, with model performance being evaluated using ROC curves, calibration plots, and Brier scores. The first model is planned to be externally validated and integrated into an electronic risk calculator with SHAP-based (Shapley Additive Explanations) visualizations to support interpretability and clinical use. By applying state-of-the-art machine learning techniques, the research aims to produce a clinically useful, interpretable model that supports early intervention and better outcomes. Although limitations such as missing data and a constrained set of predictors exist, the study is expected to make a meaningful contribution to improving geriatric diabetes care in China.