<p>Accurate and rapid point-of-care diagnostics are increasingly critical for addressing the growing global healthcare demand. Surface-enhanced Raman scattering (SERS) offers highly sensitive biofluid analysis by coupling molecular vibrational fingerprints with strong electromagnetic enhancement. However, the compositional complexity and intrinsic variability of raw biofluids yield highly heterogeneous spectra. In this review, we systematically examine recent advances in artificial intelligence (AI)-integrated SERS diagnostic platforms, focusing on their application across six major human biofluids: urine, saliva, tears, sweat, serum, and disease-specific invasively collected biofluids. Rather than merely cataloging individual studies, we critically synthesize how AI transforms high-dimensional, noisy spectral data into clinically actionable outputs without extensive sample pretreatment. We assess the relative strengths and methodological trade-offs of diverse hardware platforms and AI algorithms in overcoming matrix interference. While SERS-AI platforms demonstrate immense potential for precision diagnostics, clinical translation is hindered by methodological bottlenecks. We conclude by outlining key translational priorities, emphasizing the urgent need for standardized reporting, rigorous external validation using multicenter cohorts, explainable AI for improved model interpretability, and robust regulatory calibration strategies to establish reliable clinical decision thresholds.</p>