This paper presents a symbiotic organism search (SOS)-based support vector regression (SVR)ensemble for predicting the printed circuit board (PCB) cycle time of surface-mount-technology(SMT) production lines. Being able to predict the PCB cycle time accurately is essential for optimis-ing the SMT production schedule. Although a machine simulator can be reliably used for single-type PCB production, it is time-consuming and often inaccurate for the simulator to be applied for highlymixed orders in multiple flexible SMT production lines. Due to the dynamic changes in both PCBorders and SMT production lines, there is a diverse set of samples, but the size of similar samples isrelatively small. An SVR model is therefore used to estimate the PCB cycle time, and the SOS algorithmis employed to optimise the SVR parameters. We assume that uncertainties during the assembly process can be captured by the characteristics of PCB and SMT lines, which are utilised as features to train the SVR model. To enhance the performance of the prediction accuracy, an SOS-SVR ensembleis proposed. Experiments based on datasets collected from a leading global electronics manufacturer confirm the efficiency of the proposed approach compared to industrial solutions currently in place and other machine learning methods.