We propose a machine learning based generation of embedding vectors which are accurate enough to predict the original flip-flop toggling sequences for clock gating. Precisely, we develop a neural network model of LSTM (long short-term memory) based AE (autoencoder) model combined with SDAE (stacked denoising autoencoder) to take into account the timeseries (i.e., clock cycle) similarity feature among the toggling sequences, which is essential to determine which flip-flops should be grouped together for clock gating. By integrating (1) our LSTM based embedding vector generation model, we propose two additional ML models for clock gating: (2) joint state probability predictor (JSP) model for generating 0-state probability of two embedding vectors, and (3) joint feature predictor (JFP) model for generating a new embedding vector that combines two embedding vectors.