Cholera is a hazardous sickness due to the utilization of water polluted with the bacterium Vibrio cholerae. It remains a danger everywhere, particularly in impacted regions with unfortunate cleanliness. Cholera is an easily preventable disease that affects affected regions occasionally; therefore, accurate prediction of the disease can help prepare for necessary public health measures. This paper investigates several machine learning algorithms for cholera prediction using historical epidemiological data. The analysis used reported cholera case data, death rates, CFR, and related WHO regions obtained from several countries and years. The models used include the Support Vector Classification model, Logistic Regression model, Decision Tree classification model, and Random Forest classification. The approach taken involved collecting, cleaning, normalizing, and transforming the data used for modeling and assessing using the F1 score, accuracy, recall, ROC-AUC score, and precision. The results therefore depict that forecast of cholera epidemic by ML models can be moderate to worthwhile by depending on the ML model used in the analysis and its specific capabilities to predict the outbreaks with precision and reliability. Interestingly, the highest precision was achieved by both the Random Forest Classifier [0.82] and SVC [0.81], while the Random Forest Classifier obtained the highest ROC-AUC score [0.90], and Logistic Regression [0.87]. Based on these discoveries, leaders need to embrace data analysis to forecast the upshots of virus ailments in modern communities. Future work should attempt to incorporate more variables reflecting the degree of environmental factors and socioeconomic conditions to increase the model’s stability and accuracy. This paper presents the potential use of the machine learning technique as a foundation for cholera outbreak prediction and its specificity to assist in enhancing decision-making in public health budgeting and planning.