In this study, machine learning models were implemented, and their efficiency was evaluated to predict monkeypox diagnosis based on patient symptom data. The primary aim was to compare the performance of various classifiers, including Support Vector Machine (SVM), Decision Tree, Random Forest, Logistic Regression, XGBoost, and LightGBM. Initially, SVM demonstrated a high single accuracy score of 70.16%, but cross-validation revealed a significant drop to an average accuracy of 63.64%, suggesting poor generalization. The feature importance analysis from tree-based models was used to identify and drop less relevant features, but the improvements in performance were marginal, indicating that no single symptom strongly predicted the diagnosis. An important preprocessing step involved removing rows where both Systemic Illness was absent and monkeypox was negative, which reduced noise in the dataset and significantly improved model performance by 4–8%, particularly for Logistic Regression which has demonstrated an improvement of 8%. Gradient-boosting algorithms, such as LightGBM and XGBoost, performed closely to Logistic Regression. This research emphasizes the importance of effective data cleaning, model selection, and the use of advanced algorithms for handling complex medical datasets. Future work could explore more sophisticated feature engineering and deep learning techniques to enhance prediction accuracy in monkeypox diagnostics.