Identifying new anti-seizure medications (ASMs) is difficult due to limitations in animal-based assays. Zebrafish (Danio rerio) serve as a model for chemical and genetic seizures, but current methods for detecting anti-seizure responses are limited by incomplete detection of anti-seizure responses (locomotor assays) or low-throughput (electrophysiology, fluorescence microscopy). To overcome these challenges, we developed a novel high-throughput method using combined locomotor and calcium fluorescence data from unrestrained larval zebrafish in a 96-well plate reader. Custom software tracked fish movement and fluorescence changes (deltaF/F0) from high-speed time-series, and logistic classifiers trained with elastic net regression distinguished seizure-like activity in response to the GABA