Nanoplastics (NPs) are pervasive environmental contaminants that can cross biological barriers and accumulate in living organisms, yet their analysis is limited by a trade-off between spatial resolution and quantitative accuracy, hindering long-term studies in complex systems. Here we present a machine-learning-guided strategy to design bifunctional, internally calibrated nanoprobes that integrate fluorescence imaging with metal-based quantification. By combining aggregation-induced emission fluorescence with stable metal dopants, these probes establish a dual-signal system in which a time-invariant metal signal dynamically corrects fluorescence decay. This enables a four-dimensional quantitative framework that links fluorescence, metal, time and concentration. We show that the nanoprobes achieve >90% recovery in complex matrices and markedly improve measurement accuracy, revealing higher cellular uptake, long-term retention and transgenerational transfer of NPs than detected by fluorescence alone. These findings establish a generalizable platform for quantitative nano-bio interactions and suggest that current assessments may underestimate NPs risks in environmental and biological systems.