Objective: The paper analyzes the revenue and expenditure performance of China’s basic medical insurance fund for employees, forecasts its trends and risk status from 2026 to 2030, and provides evidence for improving dynamic fund monitoring and risk early-warning mechanisms. Methods: Annual revenue and expenditure data for China’s basic medical insurance fund for employees from 2015 to 2025 were analyzed. After harmonizing the statistical scope to account for the integration of maternity insurance, GM(1,1) models were constructed to forecast trends in fund revenue and expenditure. A Markov chain was then introduced to correct the residuals of the grey forecasting models according to their states. On this basis, the current-period balance, expenditure-to-revenue ratio, and balance rate were calculated to classify the risk level of the fund’s revenue-expenditure balance. Results: The GM(1,1) models achieved a good fit for both the revenue and expenditure series, and the Markov state-based residual correction further improved the fitting accuracy. During the forecast period, both fund revenue and expenditure are projected to continue increasing, while the current-period balance is expected to remain positive. The expenditure-to-revenue ratio is projected to range from 76.6% to 80.5%, and the balance rate from 19.5% to 23.4%. In 2027, the balance rate is projected to decline to 19.5%, placing the fund in the “caution” category and the fund is projected to remain in the “security” category in all other forecast years. Conclusion: During China’s 15th Five-Year Plan period, the basic medical insurance fund for employees is not expected to face an overt deficit risk in the short term. Although short-term revenue-expenditure risks are generally manageable, the fund’s safety margin is projected to narrow temporarily in 2027. Accordingly, 2027 should be treated as a key monitoring point within the forecast period. Actual changes in the expenditure-to-revenue ratio and balance rate should be continuously tracked, and the forecasting model and risk classification should be updated on a rolling basis as new annual data become available. These measures would help strengthen the fund’s operational risk early-warning mechanism and enhance the sustainability of the basic medical insurance system for employees.