Additives are widely employed to regulate the morphology, size, and agglomeration degree of crystalline materials during crystallization to enhance their functional, physical, and powder properties. However, the existing methods for screening and validating target additives require a large quantity of materials and involve tedious molecular simulation/crystallization experiments, making them time-consuming, resource-intensive, and reliant on the operator’s experience level. To overcome these challenges, we proposed a computer vision-assisted high-throughput additive screening system (CV-HTPASS) which comprises a high-throughput additive screening device, in situ imaging equipment, and an artificial intelligence (AI)-assisted image-analysis algorithm. Using the CV-HTPASS, we performed high-throughput screening experiments on additives to regulate the succinic acid crystal properties, generating thousands of crystal images with diverse crystal morphologies. To extract valuable crystal information from the massive data and improve the analysis accuracy and efficiency, the AI-based image-analysis algorithm was implemented innovatively for the segmentation, classification, and data mining of crystals with four morphologies to further screen the target additive. Subsequently, scale-up crystallization experiments conducted under optimized conditions demonstrated that succinic acid products exhibited a preferred cubic morphology, reduced agglomeration degree, narrowed crystal size distribution, and improved powder properties. The proposed CV-HTPASS offers a highly efficient approach for scale-up experiments. Further, it provides a platform for the screening of additives and the optimization of the powder properties of crystal products in industrial-scale crystallization processes.