This research presents a supervised learning framework with a Convolutional Neural Network (CNN) to precisely quantify dielectrophoretic force () in microfluidic devices using a regression approach. As opposed to using a classification paradigm in our prior work, here we recast the estimation task as a regression problem. Micrographs of pearl-chain arrangement in a DEP sensing device were processed using a using modified CNN architectures of AlexNet, ResNet-50, MobileNetV2, and GoogLeNet in order to predict the induced on yeast cells and polystyrene microbeads. Micrographs obtained from our dielectrophoresis (DEP) experiments with varied input voltages were preprocessed and used in building these deep regression models. prediction accuracy of the models was tested for using Mean Absolute Error (MAE), Mean Absolute Relative (MRE), Mean Squared Error (MSE), R-squared, and Root Mean Square Error (RMSE) as evaluation metrics. The results from the experiments show that the performance of our regression models had better prediction accuracy and generalization ability when compared to our classification models. ResNet-50 with RMSPROP gave the best performance, with a validation RMSE of 0.0918 on yeast cells while AlexNet with ADAM optimizer gave the best performance, with a validation RMSE of 0.1745 on microbeads. This provides a baseline for further studies in the application of deep learning and dielectrophoresis for DEP force estimation.