Process-based mechanistic models incorporate physical, chemical, and biological knowledge, providing a comprehensive understanding of climate-agriculture-management interactions. They are instrumental in addressing global environmental challenges such as climate change, food security, biodiversity conservation, and natural resource management. However, despite their utility, these models face significant limitations, including uncertainties due to model structure and parameterization, computational challenges, and the need for large-scale, high-resolution data. Machine learning, on the other hand, excels in processing large datasets and uncovering patterns, yet lacks the mechanistic interpretability of process-based models. By combining the mechanistic understanding from process-based models with the data-driven capabilities of machine learning algorithms, this chapter presents a novel approach aimed at improving forecasting precision and robustness in agricultural systems. This chapter delves into the innovative integration of process-based models with machine learning techniques to enhance the accuracy and reliability of predictions concerning soil, water, and crop dynamics. It demonstrates the efficacy of coupling process-based biophysical models with machine learning to facilitate regional simulations and evaluates the implications of this approach with a focus on crop yields and soil organic carbon dynamics under various agricultural management practices.