A process-based cotton growth model could precisely and dynamically simulate the biomass accumulation and yield formation of cotton, so as to provide technical support for smart agricultural decision-making. A dynamic prediction method for cotton growth and yield was developed by integrating meteorological data with the APSIM-Cotton model. Firstly, model parameters were calibrated based on field trial data (2023—2024). Secondly, short-term weather forecasts (ECMWF Open Data) were incorporated for 9d growth simulations. Thirdly, climate analogue years were used to construct seasonal meteorological datasets to enable the dynamic yield prediction throughout the growing season of cotton. The results showed that the APSIM-Cotton model could accurately simulate the phenology dates (NRMSE was 5.18%), biomass (NRMSE was 19.60%), and yields (NRMSE was 6.08%) of cotton under various planting densities (9~27 plants/m2) in Changji, Xinjiang. Short-term biomass predictions achieved the highest accuracy within 1~3d (NRMSE was 1.3%), then the errors were increased to about 3.24% at a 9d forecast. Integrated meteorological data (the dynamic integration of historical meteorological data, short-term weather forecasts, and historical climate analog year data) enabled seasonal yield prediction. Using 18 optimal analogue years minimized prediction errors, stabilizing yield forecast errors below 4%. However, prediction accuracy fluctuated significantly between 90d and 115d after sowing (maximum relative error was 10%), which necessitated cautious application of the prediction results during this period.