Abstract: The invasion of Spartina alterniflora (Smooth Cordgrass) in coastal wetlands has emerged as a pressing ecological challenge, significantly threatening native biodiversity and impairing critical ecosystem functions such as sediment stabilization, carbon sequestration, and habitat provision for migratory species. In response, comprehensive investigations into the spatiotemporal dynamics of this invasive species are urgently needed to inform effective ecosystem management and resource conservation strategies. This study addresses this imperative by developing an innovative biomass-based cellular automaton (CA) model specifically tailored to simulate the complex expansion patterns of S. alterniflora in salt marsh environments. Traditional CA models for vegetation dynamics often suffer from oversimplification, typically reducing vegetation presence to binary ‘on/off’ states without accounting for growth intensity or physiological responses. Our model advances this framework by incorporating biomass as a continuous variable, enabling nuanced representations of vegetation health, growth stages, and stress responses. The model employs Moore’s neighborhood rules for spatial iteration, which consider eight surrounding cells to more realistically capture seed dispersal and vegetative spread patterns. Key environmental and physiological constraints are integrated as dynamic correction factors: water depth (quantifying hydrodynamic stress), growth macrocycle (mapping individual developmental stages), and seasonal phenological changes (accounting for dormancy and active growth periods). This multi-parameter approach allows for accurate simulation of vegetation area and density distribution across heterogeneous wetland landscapes. The model was applied to the Jiangsu Yancheng Wetland National Nature Reserve, a representative coastal ecosystem, using May 2000 distribution data as initial conditions to simulate a 15-year expansion period. Validation against October 2015 remote sensing data demonstrated exceptional performance, with a Pearson correlation coefficient of 0.8247 between simulated and observed density patterns. This strong agreement confirms the model’s capacity to replicate long-term spatiotemporal dynamics with high fidelity. Furthermore, rigorous sensitivity analysis identified critical parameter thresholds for biomass accumulation rates and growth macrocycle, establishing robust operational ranges for model application in unstudied regions. The developed CA model represents a significant methodological advancement, offering valuable technical support for invasive species management in coastal wetlands. Its applications extend to informing restoration strategies, optimizing control measures, and supporting conservation planning. Beyond immediate practical utility, the model provides a novel analytical framework that can be adapted to study other invasive species or ecosystem types, thereby contributing to broader ecological research and management efforts. By bridging the gap between theoretical modeling and ecological reality, this study enhances our capacity to predict and mitigate the impacts of biological invasions on vulnerable wetland ecosystems.