The application of data-driven or machine learning models is becoming imperative in recent times to analyze manufacturing process attributes. These models use input and output datasets to evolve the relationship similar to human perceptions. The development of a reliable data-driven model is challenging due to the necessity of conducting numerous experiments, the presence of outliers and noise in the datasets, process disturbances, etc. The data-driven models can be scaled easily by accommodating new variables and attributes to evolve progressively. Alternatively, physics-based models establish an explicit relationship between process variables and desirable attributes based on scientific knowledge and a set of assumptions, but its scalability is difficult. This paper presents the development of a hybrid cutting force model for end milling operation, combining both approaches to ensure that adequate process knowledge is captured. The outcomes of the proposed method are substantiated by performing a set of computational studies and end milling experiments. © 2020 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the scientific committee of the 53rd CIRP Conference on Manufacturing Systems