One of the most important tasks in the field of medical diagnostics is epileptic seizure detection, where we need more accurate and reliable approaches for the analysis of electromagnetic EEG signals. The wavelet transform became a versatile signal processing technique that allows multi-resolution analysis with both time and frequency information. In order to obtain robust detection accuracy, choosing suitable wavelet filter banks remain a critical part. This research explores the effect of orthogonal and bi-orthogonal wavelets filter banks on epileptic seizure detection. Data of EEG signals consisting of multiple publicly available datasets were decomposed in multiple levels using the discrete wavelet transform (DWT), and features such as energy, entropy, and variance were extracted. Performance was assessed using machine learning classifiers. The orthogonal wavelet Daubechies 4 (Daub4) gives the highest accuracy, sensitivity, and specificity of 92.3%, 91.5%, and 93.8%, respectively, whereas the bi-orthogonal wavelet (Bior 4.4) gives 94.8%, 93.2%, and 95.6%, respectively. The results further show that the bi-orthogonal wavelets outperform orthogonal wavelet in terms of retention characteristics of the signal and classification performance. These characteristics come from their symmetry and edge preserving properties, both of which are crucial for identifying patterns relating to seizures. By emphasizing the need for ideal wavelet filter banks for improved seizure detection systems, the results are pushing forwards more accurate and efficient diagnostic instruments for use in clinical settings.