The neurological condition called epilepsy affects millions of people on a global scale, requiring accurate and quick diagnosis for successful treatment approaches. Current diagnostic methods produce doubtful results, prompting the need for advanced technological alternatives. We evaluate machine learning models’ capacity to revolutionize the process of epilepsy identification and prediction through electroencephalogram (EEG) signals in this research work. A robust set of algorithms, including Random Forest, Gradient Boosting, AdaBoost, XGBoost, and Stacking Regressor, enables the detection of seizures with elevated accuracy and dependable performance compared to standard methods. The evaluation shows that the Random Forest and Stacking Regressor demonstrate a strong capability for detecting seizures, while the Stacking Regressor proves most effective at predicting seizure frequency and severity. The superiority of AI-driven models emerges from a thorough evaluation through classification and regression metrics, proving them to be precise diagnostic tools. The research produces effective early intervention capabilities alongside new approaches for individualized epilepsy care, resulting in better patient outcomes for a healthy life.