Non-traditional urban data sources are reshaping public health surveillance by providing real-time, predictive, and spatially detailed health intelligence. This review synthesizes evidence on emerging data streams including wastewater monitoring, mobility and transportation data, geospatial and remote sensing systems, IoT sensors, social media signals, participatory platforms, and distributed electronic health records. Advanced analytical approaches such as hybrid machine learning models, predictive analytics, anomaly detection, and spatial modeling convert these heterogeneous streams into actionable insights for infectious disease forecasting, environmental risk assessment, food safety monitoring, and disaster response.

The analysis demonstrates that these systems enable earlier detection and more adaptive responses than conventional methods. However, significant challenges persist. Representativeness bias, algorithmic limitations, data quality issues, interoperability barriers, and infrastructure gaps limit full potential. Governance, privacy, ethical considerations, and institutional readiness emerge as critical determinants of successful implementation and equitable outcomes. The review highlights that technological innovation must be matched by robust policy frameworks, justice-centred design, and cross-sector collaboration to ensure sustainable and trustworthy urban surveillance systems.

Findings underscore a broader transition from reactive, siloed surveillance toward integrated urban health intelligence ecosystems. Realizing the full benefits will require deliberate attention to equity, interoperability standards, ethical governance, and long-term infrastructure investment. This synthesis offers practical implications for public health agencies and urban policymakers seeking to modernize surveillance in increasingly complex urban environments.