Epileptic seizures can be dangerous as they cause sudden and uncontrolled electrical activity in the brain which can lead to injuries if one falls or loss of control over physical functions. To mitigate these risks, machine learning and deep learning algorithms are being developed to anticipate seizure occurrences. Accurate prediction of seizures could enable patients to adopt preventive strategies or initiate medical interventions to halt seizures, thereby minimizing injuries and enhancing safety for individuals afflicted with epilepsy. This paper aims to combine neural networks and Ensemble learning to enhance the accuracy of diagnosing epileptic seizures. By leveraging the strengths of both techniques, the precision in seizure diagnosis can be significantly improved. It also improves the evaluation metrics used in machine learning methodologies for a more comprehensive assessment of diagnostic outcomes. This approach ensures a thorough understanding of the effectiveness of the proposed approach. In this paper, a model with a supreme precision rate is developed to detect epileptic seizures with the help of EEG signals. This study uses an ensemble method, which employs several algorithms, for instance XGB, SVM, RF, and BiLSTM. The used dataset is open access from Bonn University. The proposed methodology reached 98.52% accuracy, 97.37% precision, 95.29% recall, and 96.32% F1-score, respectively.