Integrating Artificial Intelligence With Droplet‐Based Microfluidics: Advances, Challenges, and Emerging Opportunities

Droplet‐based microfluidics has revolutionized the lab‐on‐a‐chip field by enabling precise generation and manipulation of monodisperse droplets that act as independent microreactors. Over two decades, innovations in passive geometries and active control methods have facilitated a wide range of droplet operations, driving applications in molecular diagnostics, single‐cell analysis, drug discovery, and material synthesis. Despite these advances, challenges remain in reproducibility, scalability, and detection, alongside the growing need to manage complex experimental datasets. Parallel to these developments, artificial intelligence (AI) has evolved from early neural models to powerful deep learning and foundation architectures, offering transformative opportunities for droplet‐based platforms. Supervised, unsupervised, and reinforcement learning approaches enhance droplet detection, sorting, and adaptive control, while deep learning architectures enable high‐dimensional image analysis, time‐dependent modeling, and multimodal data integration. Transfer learning and meta learning further address data scarcity, and emerging explainable AI frameworks provide interpretability critical for clinical and diagnostic applications. This review highlights the convergence of droplet‐based microfluidics and AI, examining applications across droplet generation, detection, screening, and material synthesis and offering perspectives on challenges and future directions. Together, these fields promise to accelerate discovery and expand the clinical and industrial impact of microfluidics.