This paper presents a prediction model for the process parameters of laser dressing bronze diamond grinding wheels, utilizing a BP neural network enhanced by the CIGWO algorithm. Based on the topological relationship of the BP neural network model, the number of nodes in the input layer, implicit layer and output layer of the model was determined firstly, and the mapping relationship between the process parameters and the removal amount per unit time of the grinding wheel was constructed, then the Cricle chaotic mapping adaptive weight grey wolf algorithm was used to optimise the established prediction model for the process parameters, and finally the real values of the experimental measurements were compared according to the back propagation training results. The results showed that the CIGWO-BP prediction algorithm achieves a 3.32% higher accuracy than the traditional BP network model, with an average relative error of less than 5.4% between the predicted and actual values. In summary, the optimized model offers a robust approach for predicting the removal efficiency of laser-dressed grinding wheels.