With the continuous intensification of marine plastic pollution, monitoring the transport and dispersion of microplastics has become a critical global concern. However, predicting microplastic concentrations remains highly challenging due to the lack of direct satellite signatures and the complex non-linear physical mechanisms governing their dispersion. This study develops an interpretable machine learning framework to monitor surface microplastic concentrations in the West Pacific from 2018 to 2020. We profoundly integrated Japanese AOMI in-situ microplastic observations, ERA5 meteorological/wave reanalysis, and Euro-Argo subsurface profile data utilizing a 3D Inverse Distance Weighting (3D-IDW) spatiotemporal interpolation algorithm. A Random Forest (RF) model was subsequently trained, achieving robust predictive accuracy (R² = 0.64, 0.76, and 0.87 for 2018, 2019, and 2020, respectively). Crucially, we incorporated SHapley Additive exPlanations (SHAP) to overcome the "black-box" limitations of traditional ensemble models. The SHAP analysis explicitly revealed a distinct, year-by-year regime shift in dominant environmental drivers: microplastic distribution was primarily governed by stable hydrographic and biological conditions in 2018; by dynamic wave forcing (e.g., long-period swells and Stokes drift) in 2019; and by extreme meteorological events (e.g., typhoon-induced terrestrial flushing) in 2020. Ultimately, this physics-informed framework successfully elucidates the dynamic transition of microplastic transport mechanisms between hydrographic–biological dominance and meteorological–physical forcing, providing vital scientific support for targeted pollution mitigation and coastal resilience planning.