This review explores the synergy of combinatorial microfluidics and machine learning, highlighting their transformative impact on high-throughput experimentation, closed-loop reaction optimization, and autonomous platform development. Key applications in chemical synthesis, biological research, and microfluidic design are discussed, addressing challenges and future directions for advancing healthcare and industrial innovation.

Combinatorial microfluidic systems (CMFs), including droplet-based platforms, concentration gradient generators, and valve-based architectures, enable systematic and high-throughput exploration of complex experimental spaces. These platforms generate large, multidimensional datasets at speeds and scales beyond the capacity of conventional methods. Machine learning (ML) represents a powerful way of analyzing these datasets, uncovering hidden patterns, and guiding experiments through real-time, adaptive control. This review explores the synergistic interaction between CMFs and ML, driving the development of intelligent platforms for chemical synthesis and reaction optimization, biological assays, and microfluidic device design. Emphasis is placed on closed-loop platforms where ML actively informs experimental decisions, improving speed, precision, and reproducibility. We discuss key challenges to broader adoption, including the limited scalability of microfluidic hardware, the need for standardized, high-quality datasets, and the interpretability of complex ML models. Finally, the importance of interdisciplinary collaboration among engineers, biologists, chemists, and data scientists is highlighted, alongside the development of modular design tools, curated data resources, and explainable artificial intelligence (AI). Together, these efforts are essential to realizing autonomous, ML-driven CMF platforms capable of transforming healthcare, chemical research, and industrial innovation.