In order to improve the prediction accuracy and efficiency of blasting flyrock distance, a prediction model of blasting flyrock distance based on kernel principal component analysis (KPCA) and extreme learning machine (ELM) and optimized by a whale optimization algorithm (WOA) was established. Taking a blasting operations in open-pit coal mine as an example, seven influencing factors of blasting flyrock distance were selected. KPCA was used to reduce the dimension of the non-correlation relationship between the influencing factors, and four principal components containing 95.76% of the original information were extracted as the model input. Then, WOA was used to optimize the ELM system parameters to avoid the problem of local optimal solution. Results indicate that the average relative error, root mean square error R_MSE, coefficient of determination R²and average absolute error R_MAEof KPCA-WOA-ELM model are 4.271%, 6.681, 0.985 and 6.413, respectively, which are better than those of the comparison model. KPCA-WOA-ELM model can accurately predict blasting flyrock distance, and it could provide a basis for determining the blasting safety zone in blasting operation.