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Detrimental health effects caused by the intake of food contaminated with heavy metals have drawn concerns on effective monitoring using rapid and benign methods. This work presented a novel surface-enhanced Raman scattering (SERS)-based 3-Aminobenzeneboronic acid (ABBA) labeled silver (Ag) nanosensor combined with chemometric algorithms to detect and predict total arsenic (TAs) in acid digested spiked black tea leaves. The sensor recognizes TAs through the partial detachment of ABBA and the chemical formation of As-O-Ag linkage between the TAs and the Ag nanoparticles, which caused a SERS-on signal enhancement effect. SERS combined with competitive adaptive reweighted sampling partial least squares algorithm predicted the TAs with higher correlation coefficient (R-p) results (R-p = 0.9750) and a detection limit of 0.0273 mu g/g. Good recoveries of 83.84-109.53% and the excellent agreement with the inductively coupled plasma-mass spectrometry method (R-2 = 0.999) revealed this developed rapid method could be deployed for fast-tracking of As in food samples.