Heat Release Rate (HRR) is the measure of amount of energy released per unit time from the combustion of fuel. In controlled combustion reactions, it is necessary to keep track of the HRR but it is difficult to measure it directly. In the past, researchers used flame radiation intensity, temperature, and pressure to measure HRR. But these techniques yielded results with a very low accuracy. To resolve this issue, researchers applied several machine learning methods that can precisely measure HRR at a vast scale and perform efficiently for both high and low rates. The linear correlation between HRR and the concentration of radicals in the air flame (such as OH, CH, CO, CH3, CH2O, and HCO) was also investigated. Recently, examinations on methane-air and multi-component fuel-air combustion, showed that the linear correlation between [OH]*[CH2O] and HRR fluctuates strongly, but couldn't identify flame extinctions. As of yet, no effective HRR indicator with high accuracy for a vast range of equivalence ratios could be developed. Concerning increasing emission standards, low-temperature combustion technology was highly recommended by many researchers. Combustion is a complex, spontaneous process that follows fundamental laws of chemical reac-tions. A large amount of data can be evaluated through various data mining techniques such as anticipating burning states, NOx and soot generation. ML models are derived to find and construct the HRR of premixed methane or air flames. The surmising modeling of HRR can be classified into regression problems under the guidance of machine learning. Throughout the analysis, three types of regression algorithms: artificial neural network (ANN), support vector regression (SVR), and multiple linear regression were selected to craft models of HRR. ANN comprises of three layers; input, output, and hidden. The nodes are showcased as neurons and the connections between them relate to weight values. Moreover, the activation function boosts the model's potential to explain the nonlinear relationships. For the model to work properly, the number of nodes and hidden layers must be streamlined. Activation functions are verified, and the number of nodes is looked up in each of the layers with step size 1 using Python 3.5 and Scikit-learn. SVR model is further split into two algorithms: Epsilon-Support Vector Regression(-SVR) and Nu-Support Vector Regression (Nu-SVR). Concerning this research, Epsilon-SVR is used with two kernel functions -linear and Gaussian. Kernel function, epsilon, and regularization parameter C were used to figure out the performance of SVR. In conclusion, the multiple linear regression algorithm implies parameters for a linear relationship between a label and multiple input features. To enhance the accuracy of the chemical reaction when it displays strong non-linearity, polynomial features are being used. As a result, the nonlinear representation ability increases the efficiency of the regression model.