This paper addresses the severe threat of White Spot Disease (WSD) to the shrimp farming industry in Bangladesh and proposes a comprehensive methodology for predictive modeling and early detection using advanced machine learning techniques. The study emphasizes the importance of shrimp aquaculture in Bangladesh, outlines the impact of WSD outbreaks, and sets objectives for developing a specific prediction model for shrimp farming. Employing Random Forest, Multinomial Naive Bayes, and Bagging with Decision Trees, the research achieves impressive accuracy rates exceeding 90%, with the Bagging with Decision Trees classifier and Random Forest reaching a maximum accuracy of 97.87%. The outcomes hold significant implications for early WSD identification in the shrimp farming industry, contributing to Bangladesh’s GDP and providing valuable insights for managing aquatic. The disease outbreaks globally.