Common carp (Cyprinus carpio) is one of the freshwater fisheries commodities that has important economic value, so it is widely cultured. However, common carp farming is susceptible to diseases, such as Motile Aeromonas Septicemia (MAS), which can lead to mortality. MAS disease is visually detectable, but continuous monitoring challenges arise for farmers. This research employs the YOLOv8 and DeepSORT algorithms to automatically detect common carp and wounds caused by Aeromonas hydrophila bacterial infection, obtaining wound area and swimming speed data. Training was conducted on a dataset with two different labels: infected fish wound areas and the entire fish body, using consecutive epochs of 3000 and 1000. The training results show wound label accuracy reached 91,49% for disease concentration of 107 cfu/mL and 88,68% for disease concentration of 108 cfu/mL, respectively, while the accuracy for fish label reached 96,61% for disease concentration of 107 cfu/mL and 93,44% for disease concentration of 108 cfu/mL. The coefficient value of predicted wound area against actual wound area approximates 1, indicating a close match between the two variables. The obtained fish swimming speed estimate reflects the lethargic behavior of fish due to MAS. The results demonstrate that the model is effective in detecting fish, wound areas, and swimming speed.