Abstract: Species distribution models (SDMs), grounded fundamentally in Hutchinson's ecological niche concept and biogeographical principles, serve as indispensable analytical frameworks for investigating, simulating, and projecting the potential geographical distributions of species under current and future climate change scenarios. The SDMs offer important support for scientific ecosystem management by predicting emergent spatial ecological patterns and elucidating species' potential responses to diverse environmental disturbances, including changing climatic regimes, habitat fragmentation, and anthropogenic land use changes. This comprehensive review integrates the concepts and types of SDMs and outlines prevalent methodologies and their diverse applications in ecosystem management. This paper summarizes the concepts and types of species distribution models and outlines three modeling approaches: environmental envelope techniques (e.g., BIOCLIM), which define species' fundamental niches by identifying environmental extremes from occurrence data; regression analyses (e.g., GLM, MARS), employing statistical relationships between presence/absence records and predictor variables; and machine learning (e.g., MaxEnt, RF, ANN) leveraging complex, non-linear pattern recognition capabilities. The fundamental predictive workflow consists of three critical steps: 1) data preparation—compiling species occurrence records (presence-only, presence/absence) and spatially explicit environmental variables (climate, terrain, soil); 2) model selection—choosing appropriate algorithms based on data quality and objectives; and 3) model evaluation—applying indicators (AUC, TSS) to validate prediction accuracy and robustness. These methods enable the construction of reliable predictive models that incorporate species occurrence records and relevant environmental variables to infer potential geographic distributions. The effectiveness of SDM outputs is assessed across five distinct ecosystem management contexts. Key applications demonstrated include: 1) forest ecosystems: quantifying the dynamic impacts of climate-driven and disturbance-driven changes in species ranges; 2) wetland ecosystems: predicting invasive species spread and native biodiversity threats; 3) agricultural ecosystems: delineating crop suitability shifts and pest/disease expansion risks; 4) urban ecosystems: ranking habitat suitability and assessing the potential for biodiversity conservation; 5) marine ecosystems: revealing spatial dynamics of surface-dwelling species under ocean warming. The study further highlights future directions for SDM development. These applications provide critical spatial predictions for developing proactive conservation and management strategies. The review underscores the vital role of SDMs as powerful tools for translating species-environment relationships into practical insights for ecosystem management under global change. Future development directions include: 1) enhanced integration of dynamic ecological mechanisms (e.g., dispersal, biotic interactions, adaptive evolution) to improve temporal projection realism; 2) deeper integration of multi-source data (remote sensing, genomics, citizen science) with advanced AI/machine learning to refine model accuracy and resolution; and 3) closer coupling with ecosystem service evaluation modeling to optimize conservation resource allocation. SDMs constitute indispensable tools for predicting biotic redistribution patterns in changing environments, enabling evidence-based ecosystem management strategies. Advancing SDMs along these pathways is crucial for strengthening their scientific foundation and practical utility in guiding adaptive ecosystem management.