Global food insecurity and environmental degradation creates the need to develop more sustainable agricultural solutions. Plant synthetic biology emerges as a promising yet risky avenue to develop such solutions. While synthetic biology promises enhanced crop traits, it also harbors risks of extensive environmental damage. Here, we highlight the complexities and risks associated with plant synthetic biology, while presenting the potential of multilevel mathematical modeling to assess and mitigate those risks effectively. Despite its potential, the application of multiscale mathematical models in plants remains underutilized. We advocate for the integration of technological advancements in agricultural data analysis to foster a comprehensive understanding of crops across scales. By reviewing common modeling approaches and methodologies applicable to plants, the paper sets the stage for the creation and utilization of integrated multiscale mathematical models. Leveraging modeling techniques such as parameter estimation, bifurcation analysis, and sensitivity analysis, researchers can identify mutational targets and anticipate pleiotropic effects, enhancing the safety of genetically engineered species. To illustrate the potential of the combination, we outline ongoing efforts to develop an integrated multiscale mathematical model for maize (Zea mays L.), engineered through synthetic biology to enhance resilience against Striga and drought.