Objective Soil salinization is one of the main types of land degradation, which seriously inhibits the improvement of soil quality and the growth and grain yield of crops. Reclamation of coastal land is increasingly being used as a means of raising agricultural productivity and improving food security in China. Determining the importance of potential influencing factors of soil salinization parameters and thus predicting their concentrations are important for formulating targeted control measures to improve soil quality and crop yield in tidal flat reclamation areas.Method In this study, six treatments including control (CK), organic manure (OM), polyacrylamide plus organic manure (PAM+OM), straw mulching plus organic manure (SM+OM), buried straw plus organic manure (BS+OM), and bio-organic manure plus organic manure (BM+OM) were applied to explore the effect of different reclamation treatments on different soil parameters. The effect of all treatments on soil salt content (SSC), pH, sodium adsorption ratio (SAR), and exchange sodium percentage (ESP) was analyzed and the main factors affecting the degree of soil salinization were identified. Thereafter, the multi-linear regression model (MLR), BP artificial neural network model (BP-ANN), and random forest model (RF) were conducted to predict the soil salinization parameters (SSC, pH, SAR, and ESP)using covariates, such as air temperature, precipitation, evaporation, wind speed, soil water content, soil temperature, and soil bulk density.Result The results indicated that the concentration of SSC, SAR, and ESP gradually increased, while the pH gradually decreased during the oat growing stage. All reclamation treatments effectively reduced the level of surface soil salinization. Among them, SM+OM treatment had the best inhibition effect on SSC, whereas BM+OM treatment had the best inhibition effect on soil pH, SAR and ESP. Besides, both meteorological parameters and soil properties had a significant impact on the level of surface soil salinization during the amelioration of coastal saline-alkali land. Additionally, the RF model performed much better than BP-ANN and MLR as it revealed a much higher coefficient of determination (R²) and Nash-Sutcliffe efficiency (NSE), and lower root mean square error (RMSE) than BP-ANN and MLR model.Conclusion The above results indicate that the reclamation treatments can effectively inhibit soil evaporation, improve soil structure, increase soil water holding capacity, and thus reduce the salinization level of surface soil. Our results also suggest that the RF model is a more powerful modeling approach in predicting soil salinization dynamics of coastal saline-alkali land due to its advantages in handling the nonlinear and hierarchical relationships between soil salinization parameters and covariates, and insensitivity to overfitting and the presence of noise in the data. Thus, our findings could provide a reference for predicting the soil salinization parameters in areas with similar environmental conditions.