Soil suction, an important parameter in the safety and risk assessment of geotechnical and green infrastructures, is greatly affected by plants and weather in the shallow soil layers of urban landscapes/green infrastructure. In this study, a computational model consisting of a drying-cycle model and wetting-cycle model was developed by means of a genetic programming method to depict variations in soil suction using select influential parameters. The input data in the model development were measured in a field monitoring test on the campus of the University of Macau. Soil suction was quantified by field monitoring at different distances (0.5 m, 1.5 m, and 3.0 m) from a tree, at a constant depth of 20 cm, with selected influential parameters including initial soil suction, air humidity, rainfall amount, cycle duration, and ratio of distance from tree to tree canopy. Based on the performance analysis, the efficiency and reliability of the proposed computational model are validated. The importance of each input and the coupled effect of each two input variables on the output were investigated using global sensitivity analysis. It can be concluded that the proposed computational model based on the artificial intelligence simulation method describes the relationship between field soil suction in drying–wetting cycles and select input variables within an acceptable degree of error. Accordingly, it can serve as a tool for supporting geotechnical construction design and for assessing the safety and risk of geotechnical green infrastructures.