The paper is devoted to the application of machine learning methods for epileptic seizures prediction based on features of electroencephalogram signal (EEG) and heart rate variability (HRV) parameters derived from simultaneously registered electrocardiogram signal (ECG). To study the possibilities of machine learning to predict epileptic seizures based on EEG and HRV analysis, open databases were used which contain annotations indicating the presence or absence of epileptic seizures at each minute of time. The goals of this work are prediction of epileptic seizures in terms of ictal, pre-ictal and interictal states recognition using features of brain electrical activity and HRV, and the choice of classification methods that provide the highest accuracy for this task. HRV parameters in time and frequency domains, as well as parameters of EEG signals based on energy of EEG rhythms, Hearst index, Higuchi fractal dimension and different estimations of entropy for EEG signals were used to achieve these goals. Using different sets of features, the accuracy of classifiers based on k-nearest neighbors method, decision trees, random forest classifier, multi-layer perceptron, support vector machines, and extreme gradient boosting classifier was determined.