Antimicrobial resistance (AMR) is a critical global health crisis. Traditional bacterial detection and antimicrobial susceptibility testing (AST) often fail to meet rapid clinical demands due to long turnaround times and cumbersome procedures. While microfluidic platforms offer unique advantages in high-throughput enrichment and precise bacterial manipulation, single-modal sensing remains limited by its informational dimension, leading to poor stability and high misjudgment rates in complex matrices. To address this, multimodal label-free detection integrates optical, spectroscopic, and electrical mechanisms to capture comprehensive morphological, spectral, and

electrophysiological data, significantly enhancing robustness. However, the resulting surge in multi-source heterogeneous data presents new processing challenges. Artificial intelligence (AI) has emerged as a core solution, enabling efficient signal denoising, feature extraction, strain classification, and resistance prediction through advanced data fusion. This paper reviews recent progress in multimodal label-free bacterial detection and microfluidic integration, discusses AI’s pivotal role in data processing, and addresses current challenges in model generalization and device integration. Finally, we outline future directions for miniaturized, interpretable, and clinically translatable intelligent microfluidic platforms.