Support vector regression (SVR) performs satisfactorily in prediction problems, especially for small sample prediction. The setting parameters (e.g., kernel type and penalty factor) profoundly impact the performance and efficiency of SVR. The adaptive adjustment of the parameters has always been a research hotspot. However, the substantial time cost and forecast accuracy of parameter adjustment are challenging to many scholars. The contradiction of big data prediction is especially prominent. In the paper, an SVR-based prediction approach is presented using the K-means clustering method (KMCM) and chaotic slime mould algorithm (CSMA). Eight high- and low-dimensional benchmark datasets are applied to obtain appropriate key parameters of KMCM and CSMA, and the forecast accuracy, stability performance and computation complexity are evaluated. The proposed approach obtains the optimal (joint best) forecast accuracy on 6 datasets and produces the most stable output on 3 datasets. It ranks first with a score of 0.024 in the overall evaluation. The outcomes reveal that the proposed approach is capable of tuning the parameters of SVR. KMCM, CSMA and SVR are skillfully integrated in this work and perform well. Although the performance is not outstanding in terms of stability, the proposed approach exhibits very strong performance with respect to prediction accuracy and computation complexity. This work validated the tremendous potential of the proposed approach in the prediction field.