Zoonotic pathogens pose significant global health threats. Despite this burden, establishing cost-effective early warning systems through surface water-based surveillance (SWBS) remains underexplored, particularly in low-resource settings. Here, we address this gap by analyzing fecal shedding patterns of two typical zoonotic pathogens: Chlamydia psittaci and influenza A virus (IAV) in both patients and birds. Using these data, we developed a novel theoretical framework to evaluate the feasibility of SWBS for C. psittaci and IAV in surface water. Our findings first reveal high positivity rates of C. psittaci (8-16 %) and IAV (16-84 %) were detected in cloacal swab samples, with significant variations observed among different bird species. Specifically, the highest detection rates for IAV were found in chickens (84.0 %, 84/100), ducks (70.0 %, 70/100), and geese (66.0 %, 33/50), while C. psittaci showed the highest positivity in pigeons (16 %, 8/50) and chickens (16 %, 16/100). Meanwhile, we also unravel high shedding rates in human feces (IAV: 44.7 %, 4.25 × 106 copies/g; C. psittaci: 37.6 %, 2.25 × 104 copies/g). IAV was predicted to become quantifiable in surface water once ≥29 (18-41; 50 % CI) infected birds were present nearby. Monte Carlo simulations further indicated that IAV would reach the limit of detection in the sewershed when ≥17 (7-28; 50 % CI) human infections had occurred. In contrast, C. psittaci was only reliably detectable during the peak of infection in wild-bird habitats and unlikely to be detected in the sewage from the community. We validated these predictions through prospective environmental surveillance across three sewersheds near a migratory bird habitat (January-July 2024), confirming the model’s accuracy. Our work establishes a robust, data-driven approach to optimize environmental surveillance design in resource-limited contexts, offering a scalable strategy for pandemic preparedness and early warning of infectious disease threats.