Background Cerebral Microbleeds (CMBs) serve as critical indicators of cerebral small vessel disease and are strongly associated with severe neurological disorders, including cognitive impairments, stroke, and dementia. Despite the importance of diagnosing and preventing CMBs, there is a significant lack of effective predictive tools in clinical settings, hindering comprehensive assessment and timely intervention.Objective This study aims to develop a robust predictive model for CMBs by integrating a broad range of clinical and laboratory parameters, enhancing early diagnosis and risk stratification.Methods We analyzed extensive data from 587 neurology inpatients using advanced statistical techniques, including Least Absolute Shrinkage and Selection Operator (LASSO) and logistic regression. Key predictive factors such as Albumin/Globulin ratio, gender, hypertension, homocysteine levels, Neutrophil to HDL Ratio (NHR), and history of stroke were evaluated. Model validation was performed through Receiver Operating Characteristic (ROC) curves and Decision Curve Analysis (DCA).Results The model demonstrated strong predictive performance with significant clinical applicability. Key predictors identified include the Albumin/Globulin ratio, homocysteine levels, and NHR, among others. Validation metrics such as the area under the ROC curve (AUC) and decision curve analysis confirmed the model's utility in predicting CMBs, highlighting its potential for clinical implementation.Conclusion The comprehensive predictive model developed in this study offers a significant advancement in the personalized management of patients at risk for CMBs. By addressing the gap in effective predictive tools, this model facilitates early diagnosis and targeted intervention, potentially reducing the incidence of stroke and cognitive impairments associated with cerebral microbleeds. Our findings advocate for a more nuanced approach to cerebrovascular disease management, emphasizing the importance of multi-factorial risk profiling.