At present, manual observation of the electroencephalogram (EEG) signals is the prime method for diagnosis of epileptic seizure disorders. The method is a time consuming and error prone as it involves errors due to fatigue in continuous monitoring of nonlinear and nonstationary EEG signals. Out of approximate 1% of the world’s epilepsy patients more than 25% cannot be treated correctly due to erroneous diagnosis. The automated seizure detection system can prove efficient by making the process reliable and faster. This paper reviews multi-domain feature extraction and machine learning classification techniques used in automated seizure detection systems. To analyse subtle variations in EEG, signal decomposition algorithms have been used in time, frequency, joint time-frequency, and nonlinear domain. The statistical and entropy parameters are the key features to discern normal from the seizure EEG signals. Machine learning plays a critical role in extracting meaningful information out of the extracted features. The paper also evaluates the performance of Multilayer Perceptron Neural Network, naïve Bayes, Least Square Support Vector Machine, k nearest neighbour, and random forest classifiers using sensitivity, specificity and accuracy metrics. A seizure detection technique is developed by decomposing the EEG signals by means of Tunable-Q Wavelet Transform (TQWT). To quantify the complexity of the individual multivariate sub-bands of the biomedical signals TQWT proves effective with varied values of Q factor suitable for analyzing signals with oscillatory and non-oscillatory nature. The highest accuracy of 97.3% is obtained using random forest classifier for the combination of spectral, Shannon and Kraskov entropy features. The paper compares the performance of feature extraction and classification techniques for the implemented system. The comparison explores possibility of hardware implementation of real time seizure detection scheme.