Droplet based microfluidics (DBMF) has gained huge recognition in the recent years for performing micro-reactions in droplets with high throughput, sensitivity, specificity and minimum cross-contaminations. This technology enables the researchers to realize highly reliable and rapid detection and screening applications in various fields. The high-throughput nature of droplet microfluidics generates large amounts of valuable but complex droplet dataset. Deeper analysis of this intricate droplet data is very essential for detection, classification, characterization and quantification of reactions/content inside the droplets. This can be carried out by Machine Learning (ML), which has proven itself in processing and providing deeper insights and precise predictions of relatively large amounts of complex data with shorter analysis times and exceptional accuracy. The analytical tools of ML enable to imbibe automation and control of many such diagnostic platforms, including DBMF, with minimum human intervention. In recent times, the potential of ML has been explored in microfluidic technology as well to tackle challenges in biomedical and biotechnological applications. The synergy of both the fields, DBMF and ML, helps in development of optimized and automated tools with higher accuracy for numerous applications. Specifically, this enables complete comprehension of the field to eventually realize a truly microfluidic total analysis system (µTAS). This work comprehends a general review emphasizing the implementation of different ML models with DBMF to automate various activities such as fluid control, droplet size prediction, recognition of flow pattern and identification, classification and sorting of droplets in a microfluidic device.

近年来，基于液滴的微流体 (DBMF) 因以高通量、灵敏度、特异性和最小交叉污染在液滴中进行微反应而获得了广泛的认可。该技术使研究人员能够在各个领域实现高度可靠和快速的检测和筛选应用。液滴微流体的高通量特性产生了大量有价值但复杂的液滴数据集。对这种复杂的液滴数据进行更深入的分析对于液滴内反应/内容的检测、分类、表征和量化非常重要。这可以通过机器学习（ML）来实现，它在处理和提供相对大量复杂数据的更深入洞察和精确预测方面证明了自己，分析时间更短，准确性更高。ML 的分析工具能够在最少的人工干预下实现对许多此类诊断平台（包括 DBMF）的自动化和控制。近年来，ML 在微流体技​​术中的潜力也得到了探索，以应对生物医学和生物技术应用中的挑战。DBMF 和 ML 两个领域的协同作用有助于为众多应用开发具有更高准确性的优化和自动化工具。具体来说，这可以让您完全了解该领域，最终实现真正的微流体全分析系统 (µTAS)。