Epilepsy is one of the most common neurological conditions in the world. Using sophisticated machine learning techniques, this study offers a thorough method for automated onset epileptic seizure identification with a particular emphasis on gradient boosting algorithms. To precisely identify seizure occurrences from EEG recordings, three advanced classifier models—Gradient Boosting, XGBoost, and LightGBM—were used in scientific work. For training and testing, the study uses a Siena Scalp EEG Database. Using a variety of metrics, the methodology includes thorough model training, validation, and performance assessment to guarantee accurate seizure detection. Accuracy, sensitivity, and specificity that are parameters are used for evaluation. The experimental results show that LightGBM performs exceptionally well in all three models, with accuracy, sensitivity, and specificity that are 98.9%, 99.67%, and 97.47%, respectively, the best overall results as compared with other models. While the conventional Gradient Boosting classifier continued to perform well with 97.1% accuracy, XGBoost demonstrated comparable performance with 98.3% accuracy and the highest recall rate of 99.9%. The specificity rates of LightGBM (97.5%), XGBoost (95.1%), and Gradient Boosting (92.2%) were noteworthy for each model. The field of automated seizure detection benefits from experimental work since it creates a dependable, high-performing framework that has the potential to greatly improve early seizure detection.