Identifying the key determinants of heavy metal(loid) accumulation in rice and quantifying their contributions are critical for precise prediction of heavy metal(loid) concentrations in rice and the formulation of effective pollution control strategies. The accumulation of heavy metal(loid)s in rice can be influenced by both natural and anthropogenic factors, which may interact with each other. However, distinguishing the independent roles (main effects) from interactive effects and quantifying their impacts separately pose challenges. To address this knowledge gap, we employed TreeExplainer-based SHAP and random forest algorithms in this study to quantitatively estimate the primary influencing factors and their main and interactive effects on heavy metal(loid)s in rice. Our findings reveal that soil cadmium (SCd) and rice cultivation time (C_TIME) were the primary contributors to rice cadmium (RCd) and rice arsenic (RAs), respectively. Soil lead (SPb) and sampling distances from roads significantly contributed to rice lead (RPb). Additionally, we identified significant interactive effects of SCd and C_TIME, C_TIME and RCd, and RCd and rice variety on RCd, RAs, and RPb, respectively, emphasizing their significance. These insights are pivotal in improving the accuracy of heavy metal(loid) concentration predictions in rice and offering theoretical guidance for the formulation of pollution control measures.