This research addresses the computational inefficiency in Volvo Construction Equipment's (VCE) co-simulation framework, which integrates the discrete element method (DEM) modeling of bucket-gravel interactions, which runs similar to 100 times slower than real time. We develop a deep learning surrogate to replace the DEM subsystem to predict the forces during wheel loader (WL) bucket-particle interactions. An optimized deep learning (DL) model trained with transfer learning achieves an average element-wise match accuracy [1] of 0.9903 to 0.9910 with mean squared error (MSE) of 3.3869 & times; 10-4 to 4.3427 & times; 10-4 (normalized units), while preserving the relevant sampling frequency content up to 100 Hz. Real-time capability is demonstrated by an inference time of about 6 ms per 100 Hz step, enabling a 32 s short cycle (3200 steps) to be predicted in about 19 s. Model fidelity is verified against DEM using time-series agreement and frequency, including power spectral density (PSD) comparisons, range-pair evaluation, and duty-ratio assessment (predicted duty from DL model divided by DEM duty), with duty ratios generally within the acceptable band of 0.5 to 2.0. The main limitation is that the surrogate is trained and validated on a specific gravel size range (200 to 300 mm, uniform distribution) and a single bucket configuration (5 m3). The DL model is designed for integration into the co-simulation framework, enabling real-time simulation at the DEM subsystem level and accelerating product development. Furthermore, the DL model supports force generation in site simulations and hardware-in-loop (HIL) testing.