Polymer-assisted flocculation-dewatering of mineral processing tailings (MPT) is crucial for its environmental disposal. To reduce the number of laboratory experiments, this study proposes a novel and hybrid machine learning (ML) method for the prediction of the flocculation-dewatering performance. The proposed ML method utilizes principle component analysis (PCA) for the dimension-reduction of the input space. Then, ML prediction is performed using the combination of particle swarm optimisation (PSO) and adaptive neuro-fuzzy inference system (ANFIS). Monte Carlo simulations are used for the converged results. An experimental dataset of 102 data instances is prepared. 17 variables are chosen as inputs and the initial settling rate (ISR) is chosen as the output. Along with the raw dataset, two new datasets are prepared based on the cumulative sum of variance, namely PCA99 with 9 variables and PCA95 with 7 variables. The results show that Monte Carlo simulations need to be performed for over 100 times to reach the converged results. Based on the statistic indicators, it is found that the ML prediction on PCA99 and PCA95 is better than that on the raw dataset (average correlation coefficient is 0.85 for the raw dataset, 0.89 for the PCA99 dataset and 0.88 for the PCA95 dataset). Overall speaking, ML prediction has good prediction performance and it can be employed by the mine site to improve the efficiency and cost-effectiveness. This study presents a benchmark study for the prediction of ISR, which, with better consolidation and development, can become important tools for analysing and modelling flocculate-settling experiments.