Seasonal influenza imposes a substantial public health and economic burden in the United States. The CDC estimates between 9 million and 45 million illnesses, 140,000 to 810,000 hospitalizations and 12,000 to 61,000 deaths annually, depending on epidemic severity. Notwithstanding comprehensive surveillance through the CDC's Influenza-Like Illness Network (ILINet), which encompasses over 3,000 healthcare providers across all 50 states, accurate prediction of epidemic timing and intensity remains challenging. This unpredictability complicates resource allocation and preparedness efforts. This study analyzed weekly Weighted Influenza-Like Illness (WILI) data from the 2010-2020 influenza seasons to develop reliable short-term forecasting models. Three established time-series approaches were compared: Seasonal Autoregressive Integrated Moving Average (SARIMA), state-space models, and Holt-Winters exponential smoothing. Performance was evaluated using mean absolute error (MAE), root mean squared error (RMSE) and mean absolute percentage error (MAPE) across one-week to four-week forecast horizons. Temporal analysis confirmed characteristic seasonal patterns with peak activity typically occurring between December and February and minimal activity during summer months. SARIMA models achieved the lowest forecasting errors and accurately predicted peak timing within one week for 85% of seasons. State-space and Holt-Winters methods showed reduced accuracy during epidemic surges despite adequate performance during baseline periods. These findings demonstrate that SARIMA-based forecasting systems provide actionable advance predictions to support hospital surge planning, antiviral stockpiling, targeted vaccination campaigns and public health messaging, which potentially reduce morbidity and mortality through enhanced preparedness.