Recently, machine learning, lying at the core of artificial intelligence and data science, has emerged as a promising method to yield highly reliable reaction rate constants. The machine learning methods can be in principle clarified into three categories: supervised learning, unsupervised learning and reinforcement learning, in which the supervised machine learning is usually applied in predicting chemical reaction properties by using different molecular representations as inputs. In this regard, many pioneering works have been performed to learn activation energies and minimum energy paths of chemical reactions. Meanwhile, some efforts have been paid to directly predict rate constants. For gas-phase bimolecular reactions, Houston et al. employed Gaussian Process Regression to train thermal rate constants using a dataset of 13 reactions over a large temperature range. The reactions were described by the three parameters related to Eckart tunneling, the skew angle and the symmetric stretch vibrational frequency of the reactant. The predicted rate constants averaged over the 39 test reactions were within 80% of the accurate answer. A deep neural network was employed by Komp et al. to train ∼1.5 million quantum reaction rate constants from one-dimensional model potentials, with the reactant mass and the structure of barriers as input features. The predicted logarithm of the rate product had a relative error of 1.1%. In the meanwhile, chemical reactions in liquid phase have also attracted much attention. Borhani et al. applied multiple-linear regression and artificial neural network methods to model the hydroxyl radical rate constants of water contaminants on a dataset of 457 water contaminants, using three-dimensional geometries of the molecular structures and quantum-chemically calculated descriptors as molecular descriptors. The absolute relative error of the predicted logarithmic rate constants was less than 4%. Zhong et al. combined a convolutional neural network with molecular image to model radical rate constants of water contaminants, whose predictive performance was comparable to the molecular fingerprint-deep neural network model developed by the same group. Greaves et al. predicted rate constants for organic processes in mixtures containing ionic liquids by applying multiple linear regression and artificial neural networks with descriptors taken mostly from the Dragon descriptor data base. To make the established machine learning models broadly available, Sanches-Neto et al. developed a web application "pySiRC" that predicts reaction rate constants of radical-based oxidation processes of aqueous organic contaminants by combining three machine learning algorithms with molecular fingerprints. Alkane (CnH2n+2) is an important component of various fuels, such as natural gas (mainly composed of methane), lighter fuel (e.g., n-butane), motor gasoline (consisting of various compounds of alkane and aliphatic hydrocarbon) and aviation fuel. In the case of aviation fuel, the combustion is largely proceeded by abstracting hydrogen atoms from different sites of alkanes by free radicals such as O, H, OH, HO2, CH3 and so on. Accurate measurement of thermal rate constants of combustion reactions is very challenging and even infeasible at high temperatures. Compared to the vast number of reactions associated with the combustion process, there are only a few simple reactions whose rate constants have been measured. Theoretically, transition state theory (TST), Rice-Ramsperger-Kassel-Marcus theory and master equation approaches, could provide powerful and useful supplements. However, they may not be practical as the number of atoms involved in the reaction increases. To address this challenge, our group proposed using multilayered neural network models to predict rate constants of hydroxyl radical reactions with alkanes, with the reactions initially represented by three topological indices for the molecular structure of alkane and the index for the broken C-H bond. Furthermore, we developed a novel hybrid machine learning model by combing feedforward neural network with eXtreme gradient boosting (XGB-FNN) to predict rate constants of reactions between alkanes and CH3 radical. The model employed six descriptors, including temperature, activation energy, and four Mordred generated descriptors that were selected through Pearson correlation analysis. The average deviation of the XGB-FNN model on the prediction set was about 40%. All these studies have shown that machine leaning models are capable of accurately predicting rate constants of combustion reactions with well-designed molecular descriptors.