Efficient database size management is crucial for organizations relying on on-premise SQL Server databases. Unexpected database growth can lead to server space saturation, causing performance degradation, system downtime, and increased operational costs. This thesis presents a forecasting approach to predict database growth, enabling proactive resource allocation and preventing storage-related failures. The study begins by extracting databases hosted on the given SQL Server instance and retrieving historical row byte size data from the InfluxDB time-series database for the past year. The extracted data undergoes cleaning and transformation to prepare it for time-series forecasting using the SARIMAX (Seasonal AutoRegressive Integrated Moving Average with eXogenous factors) model SARIMAX is chosen over other time-series forecasting models due to its ability to handle seasonality, trends, and external influencing factors, making it well-suited for database growth predictions.