Detecting cognitive decline early in the older adults is crucial for effective intervention. This study, part of the Ma'anshan Healthy Aging Cohort Study, examined 2,288 participants with normal cognitive function. Forty-two potential predictors, including demographics, chronic diseases, lifestyle factors, and baseline cognitive function, were selected. The dataset was divided into training, validation, and test sets (60%, 20%, and 20%, respectively). Recursive feature elimination (RFE) and six machine learning algorithms were used for model development. Model performance was assessed using area under the curve (AUC), specificity, sensitivity, and accuracy. SHapley Additive exPlanations (SHAP) was applied for interpretability, revealing the top ten influential features: baseline MMSE, education, economic status, social activities, PSQI, BMI, SBP, DBP, IADL, and age. The Naïve Bayes (NB) algorithm-based model achieved an AUC of 0.820 (95% CI 0.773-0.887) on the test set, outperforming other algorithms. This model can help primary healthcare staff in community settings identify individuals at higher risk of cognitive impairment within three years among older adults.