Abstract Macular edema induced by the accumulation of fluid is associated with the most vision-threatening retinal diseases. Optical coherence tomography (OCT) has become the de facto standard of care in assessing retinal fluid and image-guided treatment management. Deep learning has made an impact across medical image analysis domains and many effective retinal OCT analysis methods have been proposed. This chapter summarizes recent work and provides an overview of the state of the art of machine and deep learning algorithms for detecting and segmenting retinal fluid on OCT. We also demonstrate the benefits such automated methods bring in the form of two clinically relevant applications that are enabled by having retinal fluid accurately quantified.