INTRODUCTION: Widely used in livestock breeding, residues of antibiotic drugs in milk have become a threat to food safety and human health. Current rapid detection technologies using colorimetric immunochromatographic strip tests (IST) lack the necessary sensitivity for on-site trace monitoring. Fluorescence-based detection in the near-infrared IIa' (NIR-IIa') region (1000 ∼ 1300 nm) has enormous potential due to greatly minimized auto-fluorescence and light scattering. OBJECTIVES: The aim of this work is to develop an ultrasensitive IST platform using NIR-IIa' fluorescent nanoparticles as labels for multiplex antibiotic residues detection in milk. METHODS: NIR-IIa' fluorescent nanoparticles were assembled by encapsulating synthesized NIR-IIa' fluorophores into carboxyl - modified polystyrene nanoparticles. The NIR-IIa' nanoparticles were subsequently used as labels in an IST platform to detect sulfonamides, quinolones, and lincomycin simultaneously in milk. A portable fluorescent reader was fabricated to provide on-site detection. To further validate the developed IST platform, the detection was compared with LC-MS/MS in 22 real milk samples. RESULTS: Fluorescent nanoparticles were synthesized with low energy emission (1030 nm) and large Stokes shift (>250 nm) showing a much higher signal-to-noise ratio compared with fluorophores emitting in the NIR-I region. The developed IST platform yielded a highly sensitive, simultaneous quantification of sulfonamides, quinolones, and lincomycin in milk with detection limits of 46.7, 27.6 and 51.4 pg/mL, respectively, achieving a wide detection range (up to 50 ng/mL). The IST platform showed good accuracy, reproducibility, and specificity with the portable fluorescent reader which could rapidly quantify in 10 s. These results were better than reported immunochromatographic assays using fluorescent labels, and remarkably, showed a higher recognition ability than LC-MS/MS for real samples. CONCLUSION: The utility of NIR-IIa' fluorescence-based IST platform for the fast, sensitive, and accurate detection of antibiotics in milk was demonstrated, successfully verifying the potential of this platform in detecting trace materials in complex matrices.