Epilepsy is a neurological disorder that causes repeated seizures. One of the biggest problems in accurately classifying epilepsy is that there are not enough samples in the minority class, which makes the dataset unbalanced. This can lead to biased model performance, particularly in predicting the minority class. To address the issue of imbalanced datasets, we propose a framework utilizing an Extreme Learning Machine (ELM) enhanced with Principal Component Analysis (PCA) and Synthetic Minority Over-sampling Technique (SMOTE) to improve classification performance for epilepsy. PCA reduces the dataset's dimensionality while retaining essential data, to improve the discriminative ability of the features used by the classification model. SMOTE ensures that the minority class samples are properly represented, allowing the model to learn their distinguishing features effectively. We compare the performance of ELM on the imbalanced dataset with and without balancing techniques and PCA. We evaluate the classification accuracy, precision, recall, and F1-score to assess the effectiveness of our proposed approach. Our experiments demonstrate that the PCA-ELM model, when combined with SMOTE, outperforms other models such as Support Vector Machine (SVM) and ELM kernel based Support Vector Machine (SVM-ELM). The optimized hyperparameters obtained through the grid-search optimization algorithm further enhance the performance of the algorithms. The findings of this study have important implications for developing more effective diagnostic tools and personalized treatment strategies for epilepsy patients.