Here, we build, train and apply an automated imaging and deep learning image analysis pipeline for optimization of assembly and culture conditions for miniaturized 3D cell spheroids production in microfluidic droplets. Miniaturization of spheroids, rapid assembly optimization and automated spheroid analysis would amount to a paradigm shift in early drug development. We expand an automated ultra-high-throughput workflow for minispheroid production in microfluidic droplets by training a convolutional neural network (CNN) model for automated minispheroid morphology assessment and classification. The CNN classifier was used to characterize minispheroid assembly of three different cell lines for a range of incubation times and surfactant concentrations.