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The purpose of this comparative study is to improve the predictive accuracy of the cutting force during the turning of Ti-6Al-4V on a lathe machine. By optimizing the machining process parameters such as cutting speed, feed rate, and depth of cut, the cutting force in the machining process can be improved significantly. Cutting force is one of the crucial characteristics that must be monitored during the cutting process in order to enhance tool life and the surface finish of the workpiece. This paper is based on the experimental dataset of cutting forces collected during the turning of titanium alloy under the Minimum Quantity Lubrication (MQL) condition. To predict the cutting forces, two machine learning techniques are explored. Firstly, a black-box model called an Artificial Neural Network (ANN) is proposed to predict cutting force. Using the Levenberg-Marquardt algorithm, a two-layered feedforward neural network is built in MATLAB to predict cutting force. The second model to be implemented was the Genetic Algorithm (GA), a white-box model. GA is an optimization technique which is based on Darwinian theories. It is a probabilistic method of searching, unlike most other search algorithms, which require definite inputs. Using symbolic regression in HeuristicLab, a GA model is developed to estimate cutting force. The anticipated values of cutting forces for both models were compared. Since the ANN model had fewer errors, it was ascertained that the particular model is preferable for machining process optimization.