Fresh produce supermarkets play a vital role in modern cities, but their management is challenging due to the perishable nature of vegetables. This research proposes and implements an automated pricing and replenishment strategy based on hybrid ML models and massive historical sales data. The combination of Seasonal ARIMA, Linear Regression, and Gradient Boosting Decision Tree (GBDT) results in an average R² for different categories of vegetable products of 0.993, indicating high model accuracy and fitting, which could guide pricing and replenishment strategies for the merchant who sells time-sensitive products.