In medical oncology, ovarian cancer identification is correct and timely continues to be a chief difficulty. To enhance model overall performance, this study indicates a machine learning-based technique for ovarian cancer classification that uses state-of-the-art hyperparameter optimization techniques. On publicly reachable ovarian cancer datasets, numerous algorithms, consisting of Support Vector Machines, Random Forest, and XGBoost, had been assessed. To discover the high-quality parameter settings for every model, hyperparameter tuning turned into achieved the usage of grid search, random search, and Bayesian optimization. Classification overall performance turned into evaluating the usage of metrics like accuracy, precision, recall, F1-score, and AUC-ROC. The findings showed that hyperparameter tuning significantly improved the accuracy and resilience of the model, with the XGBoost model performing at its first-rate after optimization. The recommended approach emphasizes the value of methodical tuning in growing truthful diagnostic models and the promise of machine learning to assist early detection of ovarian cancer, enhancing patient consequences via prompt intervention.