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Oil agglomeration, as an efficient technique for coal slime cleaning and upgrading, was employed to separate coal slime with an ash content of 19.08% in this work. The optimum oil type, the pulp density, the oil dosage, and the agitation rate were determined at the dodecane, 12%, 24%, and 1600 r/min, respectively. The response surface methodology (RSM) was adopted to investigate the interactions between various operational factors on the response of combustible material recovery, efficiency index, and ash rejection. By considering the interactions among operational factors, the agglomeration achieved improvement, given small oil consumption, medium agitation rate, and high processing capacity, through optimized operational conditions. Moreover, a prediction model with a higher prediction accuracy for the efficiency index of coal slime oil agglomeration was established based on the artificial neural network (ANN). This work provides an experimental foundation for the operation design and process optimization in the oil agglomeration of coal slimes.