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Cutting force is one of the fundamental elements that can provide valuable insight in the investigation of cutter breakage, tool wear, machine tool chatter, and surface finish in face milling. Analyzing the relationship between process factors and cutting force is helpful to set the process parameters of the future cutting operation and further improve production quality and efficiency. Since cutting force is impacted by the inherent uncertainties in the machining process, how to predict the cutting force presents a significant challenge. In the meantime, face milling is a complex process involving multiple experts with different domain knowledge, collaborative evaluation of the cutting force model should be conducted to effectively evaluate the constructed predictive model. Gene Expression Programming (GEP) combines the advantages of the Genetic Algorithm (GA) and Genetic Programming (GP), and has been successfully applied in function mining and formula finding. In this paper, a new approach to predict the face milling cutting force based on GEP is proposed. At the basis of defining a GEP environment for the cutting force prediction, an explicit predictive model has been constructed. To verify the effectiveness of the proposed approach, a case study has been conducted. The comparisons between the proposed approach and some previous works show that the constructed model fits very well with the experimental data and can predict the cutting force with a high accuracy. Moreover, in order to better apply the constructed predictive models in actual face milling process, a collaborative model evaluation method is proposed to provide a distributed environment for geographical distributed experts to evaluate the constructed predictive model collaboratively, and four kinds of collaboration mode are discussed.