Influenza A virus (IAV) has become increasingly unpredictable in its seasonal patterns following the COVID-19 pandemic. A key challenge remains identifying optimal moments for public health interventions to inform evidence-based decisions. Real-time PCR was employed to quantify IAV concentrations in wastewater samples from 38 treatment plants in Shenzhen, China (n = 2,764), collected weekly from March 2023 to March 2024. The random forest model was used to estimate IAV infections based on viral concentrations and physico-chemical parameters. Baseline IAV concentrations were established using mean, geometric, and median values, revealing a seasonal IAV pattern with peaks in winter 2023 and spring 2024. The optimized random forest model (mean absolute error = 2,307, R2 = 0.988) integrated IAV concentration, flow rate, wastewater temperature, chemical oxygen demand, total nitrogen, and phosphorus. The established baseline values of IAV concentration provides crucial evidence for optimal timing (early March and mid-November) of seasonal vaccination and nonpharmaceutical interventions. The baseline defined as the median provided 2 to 4 weeks early warning before influenza season onset (≥ 100 cases/100,000). The median baseline also allows early detection of abnormal increases in respiratory syncytial virus, supporting timely public health interventions. Wastewater surveillance of IAV, which encompasses the entire population, closely aligns with clinical monitoring trends, underscoring its vital role as a complementary tool in public health. The estimated number of infections and the baseline defined by median offer valuable insights to guide vaccination timing and non-pharmaceutical interventions, ultimately improving community health and disease control efforts.Highlightsλ By integrating clinical surveillance and wastewater monitoring data, dynamic baselines were established to detect real-time IAV concentration changes, enabling the early identification of epidemic risks.λ IAV baselines in wastewater guide optimal timing for vaccination and nonpharmaceutical interventions, especially in developing counties.λ The Random Forest model accurately predicts IAV cases using key physico-chemical parameters like IAV concentration, flow rate, wastewater temperature, chemical oxygen demand, total nitrogen and phosphorus.