Introduction
Public health surveillance is crucial for decision-making. Given the significant threat of influenza to public health, developing predictive models using multichannel surveillance systems is imperative.

Methods
Data were collected from multichannel surveillance systems, including hospitals, search engines, and climatological and air pollutant surveillance systems, in a southern Chinese city from January 2023 to January 2025. Spearman’s correlation analysis assessed the relationships between variables and reported influenza cases. Several machine learning models were used to predict trends in reported cases.

Results
Correlation analysis showed that all four surveillance systems were related to influenza, with 27 variables correlated with daily reported cases. The Long Short-Term Memory model, established based on variables with the highest lagged correlations (5-day to 7-day lag) through combined surveillance systems, outperformed other models for 5-day forecasts (R2=0.92; mean absolute error=156.92; mean absolute percentage error=79.95%; root Mean Squared Error=292.33).

Conclusions
Data from various surveillance systems effectively track influenza epidemics. The model shows potential for infectious disease surveillance and epidemic preparedness.