Shrimp farming is a rapidly growing industry worldwide. However, disease outbreaks are a major challenge for shrimp farmers and can cause significant economic losses. Traditional methods for diagnosing shrimp diseases are often time-consuming and labor-intensive. Machine learning algorithms can be used to analyze and classify data regarding shrimp diseases. This can help identify patterns in disease outbreaks, as well as potential risk factors and effective management strategies. In this article, a machine learning-based method will be intended to be developed for diagnosing diseases affecting shrimps. By analyzing historical data on disease outbreaks and environmental conditions, these models can help predict the likelihood of future outbreaks and inform management strategies to prevent or mitigate their impact. This can help farmers quickly identify and respond to disease outbreaks, potentially reducing mortality rates and improving overall productivity. A dataset containing information on healthy shrimp and shrimp infected with different diseases will be used for modeling the application. The dataset will be pre-processed and augmented to ensure that the machine learning models can accurately predict the outbreak of shrimp diseases. The performance of these models will be evaluated, and the one with the finest results will be selected. This research assists aqua farmers in making informed decisions to stop the virus from spreading to neighboring ponds.