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Background: Zebrafish-based drug discovery systems provide significant advantages over mammalian models for high-throughput in vivo screening. Among these, the NF-κB eGFP reporter system significantly enhances drug discovery in zebrafish by enabling real-time, high-resolution monitoring of pathway activity in live organisms, thereby streamlining mechanistic studies and high-throughput screening. Methods: We developed a novel AI (Quantifish and Orange software)-based zebrafish precision individualized 96-well ZF plates (0–7 dpf) and individualized MT tanks (8 dpf–4 mpf) protocol for the transposon-mediated transgenesis of the NFκB eGFP reporter system. Results: One-cell stage embryos were administered NFκB reporter construct and Tol2 transposase mRNA via microinjection and transferred to separate wells of a 96-well ZF plate. Bright-field and fluorescence images of each well were captured at 5 dpf in the F0, F1, and F2 generations using the automated confocal high-content imager CQ1. The Quantifish software was used for the automated detection and segmentation of zebrafish larval fluorescence intensity in specific regions of interest. Quantitative data on the fluorescence intensity and distribution patterns were measured in Quantifish, and advanced statistical and machine learning methods were applied using Orange. Imaging data with eGFP expression results were assessed to evaluate the efficiency of the transgenic protocol. Discussion: This AI-enhanced precision protocol allows for high-throughput screening and quantitative analysis of NFκB reporter transgenesis in zebrafish, enabling the efficient identification and characterization of stable transgenic lines that exhibit tissue-specific expression of the NF-κB reporter, such as lines with induced expression restricted to the retina following LPS stimulation. This approach streamlines the evaluation of regulatory elements, enhances data consistency, and reduces animal use, making it a valuable tool for zebrafish drug discovery. Full article