To investigate the growth characteristics and patterns of largemouth bass （Micropterus salmoides） during pond cultivation， various growth parameters including length， total length， snout length， eye diameter， head length， caudal peduncle length， head height， body height， caudal peduncle height， body width， and body mass were measured from individuals ranging from （16.3±4.9） g to （424.9±27.2） g.The correlations among these growth parameters were analyzed， and the predictive models for body mass were constructed using support vector regression （SVR）， radial basis function neural network （RBF）， and random forest regression （RF）.The best-fit model was determined by comparing the predicted values with the actual measured values.Optimal growth models were also developed for each growth parameter using model-fitting approach.The results revealed a highly significant correlation between body mass and growth parameters.The SVR-based predictive model exhibited the highest accuracy， with a coefficient of determination （R²） of 0.996， a root mean square error （RMSE） of 9.004， and a mean absolute error （MAE） of 6.598.A power function relationship was observed between body mass and body length， with an equation of W=0.0127×L^3.224 and a R² of 0.977.The Logistic models were the best for total length， body length， snout length， and head length.Von Bertalanffy models were the best models for head height， body height， eye diameter， and body width， while Gompertz models were most suitable for body mass， caudal peduncle length， and caudal peduncle height.The condition factor of largemouth bass fluctuated from 2.26% to 2.93% during the cultivation period.These findings suggest that growth models and body mass predictive models can be utilized to understand the growth process of largemouth bass under pond-cultured conditions.Accurate feeding based on these models can lead to optimal cultivation outcomes.