Non-invasive and continuous spatiotemporal pathogen monitoring at biological interfaces (e.g., human tissue) holds promise for transformative applications in personalized healthcare (e.g., wound infection monitoring) and environmental surveillance (e.g., airborne virus surveillance). Despite notable progress, current receptor-based biosensors encounter inherent limitations, including inadequate long-term performance, restricted spatial resolutions and length scales, and challenges in obtaining multianalyte information. Surface-enhanced Raman spectroscopy (SERS) has emerged as a robust analytical method, merging the molecular specificity of Raman spectroscopy's vibrational fingerprinting with the enhanced detection sensitivity from strong light-matter interaction in plasmonic nanostructures. As a receptor-free and noninvasive detection tool capable of capturing multianalyte chemical information, SERS holds the potential to actualize bio-interfaced spatiotemporal pathogen monitoring. Nonetheless, several challenges must be addressed before practical adoption, including the development of plasmonic bio-interfaces, sensitive capture of multianalyte information from pathogens, regeneration of nanogap hotspots for long-term sensing, and extraction of meaningful information from spatiotemporal SERS datasets. This dissertation tackles these fundamental challenges. Plasmonic bio-interfaces were created using innovative nanoimprint lithography-based scalable nanofabrication methods for reliable bio-interfaced spatiotemporal measurements. These plasmonic bio-interfaces feature sensitive, dense, and uniformly distributed plasmonic transducers (e.g., plasmonic nano dome arrays, optically-coupled plasmonic nanodome and nanohole arrays, self-assembled nanoparticle micro patches) on ultra-flexible and porous platforms (e.g., biomimetic polymeric meshes, textiles). Using these plasmonic bio-interfaces, advancements were made in SERS signal transduction, machine-learning-enabled data analysis, and sensor regeneration. Large-area multianalyte spatiotemporal monitoring of bacterial biofilm components and pH was demonstrated in in-vitro biofilm models, crucial for wound biofilm diagnostics. Additionally, novel approaches for sensitive virus detection were introduced, including monitoring spectral changes during viral infection in living biofilms and direct detection of decomposed viral components. Spatiotemporal SERS datasets were analyzed using unsupervised machine-learning methods to extract biologically relevant spatiotemporal information and supervised machine-learning tools to classify and predict biological outcomes. Finally, a sensor regeneration method based on plasmon-induced nanocavitation was developed to enable long-term continuous detection in protein-rich backgrounds. Through continuous implementation of spatiotemporal SERS signal transduction, machine-learning-enabled data analysis, and sensor regeneration in a closed loop, our solution has the potential to enable spatiotemporal pathogen monitoring at the bio-interface. / Doctor of Philosophy / Continuous monitoring of pathogens within our bodies and surrounding environments is indispensable for various applications in personalized healthcare (e.g., monitoring wound infections) and environmental surveillance (e.g., airborne virus tracking). To accomplish this, we require sensors capable of seamlessly interfacing with biological systems, such as human tissue, and consistently providing pathogen-related information (e.g., spatial location and pathogen type) over prolonged periods. Our research relies on Surface-enhanced Raman spectroscopy (SERS) to address this challenge. SERS enables noninvasive sensing by providing unique fingerprints of molecules near the sensor's surface. SERS holds the potential to enable bio-interfaced spatiotemporal pathogen monitoring, but several challenges must be tackled before practical adoption. In this dissertation, we address various fundamental challenges in SERS, including constructing SERS devices that can seamlessly interface with biological systems while maintaining performance, sensitively capturing pathogen-related information, extracting meaningful insights from SERS datasets, and continuously regenerating the sensor surface to ensure long-term performance. We developed SERS devices capable of seamlessly interfacing with biological systems using innovative scalable nanofabrication methods. These devices contain sensitive, dense, and uniformly distributed SERS sensors on flexible and porous platforms, such as polymeric scaffolds and textiles. Leveraging these SERS devices, we made advancements in pathogen sensing, data analysis, and sensor regeneration. We demonstrated large-area spatiotemporal monitoring of biofilm components and pH in lab-grown biofilm models, critical for wound biofilm diagnostics. Additionally, we introduced novel approaches for sensitive virus detection, including monitoring changes in SERS signals during viral infection in living biofilms and directly detecting decomposed viral components. The SERS datasets were analyzed using machine learning models to extract biologically relevant spatial and temporal information, such as the spatial location of pathogen components and the temporal stage of pathogen growth, and to predict biological outcomes. Finally, we developed a sensor regeneration method to enable long-term continuous detection in complex backgrounds, such as blood. By continuously performing spatiotemporal pathogen sensing, data analysis, and sensor regeneration in a closed loop, our solution has the potential to realize bio-interfaced spatiotemporal pathogen monitoring.