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Normal and visually-impaired zebrafish larvae have differentiable light-induced locomotor response (LLR), which is composed of visual and non-visual components. It is recently demonstrated that differences in the acute phase of the LLR, also known as the visual motor response (VMR), can be utilized to evaluate new eye drugs. However, most of the previous studies focused on the average LLR activity of a particular genotype, which left information that could address differences in individual zebrafish development unattended. In this study, machine learning techniques were employed to distinguish not only zebrafish larvae of different genotypes, but also different batches, based on their response to light stimuli. This approach allows us to perform efficient high-throughput zebrafish screening with relatively simple preparations. Following the general machine learning framework, some discriminative features were first extracted from the behavioral data. Both unsupervised and supervised learning algorithms were implemented for the classification of zebrafish of different genotypes and batches. The accuracy of the classification in genotype was over 80 percent and could achieve up to 95 percent in some cases. The results obtained shed light on the potential of using machine learning techniques for analyzing behavioral data of zebrafish, which may enhance the reliability of high-throughput drug screening.