Suspended sediment load (SSL) is essential to river and dam engineering. Due to the complexity and stochastic nature of sedimentation, SSL prediction is a challenging task and conventional methods often fail to generate accurate results. Aiming to provide an improved estimation, this paper contributes to a new forecasting framework by integrating the seasonal adjustment (SA) and Bayesian optimization (BOP) into a machine learning (ML) model (denoted as BMS). The SA is used for de-seasonalisation and trend extraction; the BOP is to optimize the ML architecture. The BMS is evaluated using the daily SSL records from the Yangtze River. Its performance is appraised by statistical criteria of Nash-Sutcliffe efficiency (NSE), correlation coefficient (CC), root mean squared error (RMSE) and mean absolute error (MAE). With the de-noising and hyper-parameter tuning modules, the BMS effectively heightens the accuracy of the standard ML models. The most significant improvement occurs in the Boosting model, with its augments in NSE and CC by 4.3% and 1.6%, and reductions in RMSE and MAE by 24.9% and 24.2%. The BMS gains by comparison even under flood conditions, where it remarkably reduces the errors of the constituent models by up to 47.9% for RMSE and 48.3% for MAE.