Electroencephalogram recordings are the mainstay of automated epileptic seizure detecting methods for clinic diagnosis. These recordings are incredibly valuable resources for locating and identifying epileptic seizures. Presently, for typical nonlinear and dynamic signal, conventional seizure detecting techniques based solely on one view characteristics have significant drawbacks. This research aims to examine the impact of multiview feature collection and multiple level spectral analysis techniques on seizure detection signal detection. In order to gather sufficient signal information, multiview characteristics are derived from time domaining, the frequency domaining, and the information concepts. Additionally, feature collection technique for the automated seizure identification based on ant colony optimization approach (ACOA) is put forth. Furthermore, for multilevel spectral analysis, the signals are separated into four types of brain wave because of their distinct frequency components. The four rhythm wave effects on the seizure detections are contrasted. To identify signals related to seizure or non-seizure episodes, three popular classifiers are used. According to the outcome, the database’s average classification accuracy, specificity, and sensitivity are 98.14%, 98.64%, and 96.79%, respectively. The classifier’s accuracy increases by 5.99% when the ACOA-based feature collection approach is employed for automated seizure detections. When compared to most advanced techniques, the suggested strategy performs exceptionally well for automated seizure detections. Further, it is demonstrated that crucial stage in seizure detections is the feature selection approach. The waves in frequencies of 5–8 Hz exhibit superior output in detection of signal and are more appropriate for this technique when using ACOA-based feature collection and multilevel spectral analysis. For automatic seizure identification, the ACOA-based feature selection algorithm can be a helpful auxiliary tool in clinic diagnosis.