<![CDATA[Background: Leptospirosis is a global zoonotic disease heavily driven by geospatial environmental factors. While GIS and spatial statistical methods are increasingly used for risk mapping, a comprehensive synthesis of their integration within public health surveillance systems remains limited.Objective: This systematic review aims to synthesize literature to identify dominant geospatial environmental risk factors, examine diverse spatial modeling methods, and assess the applicability of risk mapping outputs for public health surveillance and decision-making.Methods: Systematic searches were executed across Scopus, Google Scholar, and citation tracking (2020–2026) using the PEO framework .Study screening followed PRISMA guidelines, and methodological quality was formally evaluated using the Joanna Briggs Institute (JBI) Critical Appraisal Checklist.Results: Of 55 eligible studies (47 appraised as High Quality, 8 as Medium Quality), key risk factors identified included proximity to water, rainfall, land cover, humidity, and population density. The reviewed studies successfully integrated various spatial statistical methods—including Moran's I, LISA, SaTScan, MaxEnt, and Bayesian modeling—within GIS platforms (primarily QGIS) to map risk surfaces and hotspots.Conclusion: Integrating spatial statistical methods within a QGIS environment provides an open, systematic, and replicable platform to enhance early warning systems, strengthen area-based surveillance, and facilitate evidence-based public health decision-making.]]>
