Accurate prediction of cutting forces in milling is crucial for intelligent machining. However, traditional methods of collecting discrete data are often time-consuming and can lead to overfitting due to data sparsity. This study introduces a continuously variable data collection strategy designed to efficiently gather training data for machine learning models. By linearly varying the feed rate and radial depth of cut along a single tool path, we obtained continuous data that reflect the variations in machining conditions. Additionally, we applied trigonometric feature transformation to the tool rotation angle to maintain its periodic characteristics. The collected data were used to train Artificial Neural Network (ANN), Support Vector Regression (SVR), and Gaussian Process Regression (GPR) models. The results demonstrate that models trained with continuous data exhibit improved generalization performance under unseen conditions compared to those trained with discrete data. Furthermore, comparable predictive accuracy was achieved using a subset of the continuous dataset. These findings suggest that the proposed approach enhances data efficiency in model training and has the potential to reduce the experimental effort required in developing machining monitoring systems.