The influence exerted by landscape fragmentation on various species is intricately tied to the properties of individual patches, which encompass a multitude of factors such as the type of patch, its area, shape, and the dynamic interactions these patches maintain with their neighboring counterparts. Within the specific context of fragmented agricultural landscapes, the exact impact of these patch properties on key species is a domain that still remains shrouded in a veil of limited understanding and empirical clarity. This study pivots its focus towards a representative agricultural landscape located in the Hainan region of China, a landscape that is notably marked by substantial fragmentation. In this particular context, a meticulously conducted field investigation spanned across 180 patches, each serving as a distinct habitat for three critical species: pests (Xyleborus affinis), pollinators (Apis cerana), and natural enemies (Trichogrammatid ostriniae Pang et Chen). The properties of each patch, encompassing elements such as type, area, shape, and the intricate interplay with adjacent patches, were rigorously documented and analyzed. Subsequently, to decipher the complex relationships between the properties of these patches and the abundance of the aforementioned species, the capabilities of three advanced machine learning models were harnessed: namely, the Artificial Neural Network, the Random Forest, and the Support Vector Regression models. These models were adeptly employed, leveraging their respective analytical strengths, to analyze the intricate relationships at play. The results of this comprehensive study uncovered a significant revelation: the robust Random Forest model emerged as a superior predictor of the abundance of all three species in comparison to the Support Vector Regression and the Artificial Neural Network models. This was evidenced by impressively high R-squared values: 0.785 for X. affinis, 0.845 for A. cerana, and 0.798 for T. ostriniae. These findings underscored the paramount importance of certain patch properties, most notably the type of patch, in influencing the abundance of these species. In particular, it was observed that patch area exerted a dominant influence on the abundance of T. ostriniae, with subtle yet crucial interactions influencing the abundance of X. affinis and A. cerana, thereby surpassing the significance of other patch properties. Furthermore, variations in patch types revealed intriguing patterns. Notably, the study unveiled that rubber plantations harbored flourishing communities of X. affinis and A. cerana, a finding that was markedly pronounced when contrasted with the abundance found in natural forests and other patch categories. In stark contrast, T. ostriniae exhibited its highest abundance in the pristine sanctuaries of natural forests and cultivated landscapes. Overall, this study not only reveals the differential impacts of patch properties on the richness of multiple species but also elucidates the critical role that specific patch types play for different species. Additionally, it validates the efficacy of machine learning as a potent and insightful tool for inferring the impacts of landscape fragmentation and patch properties on species abundance and diversity.