Pyrolysis kinetics is one way to produce bio-oil and biochar from a biomass product. It is a method to harvest clean energy from a biomass product. Moreover, kinetics and thermal composition of the biomass product is essential for pyrolysis design and optimization. However, industrial pyrolysis process is up to 200°C/min and lab scale pyrolysis temperature is up to 100°C/min. In this study, data from thermogravimetric analysis (TGA) has been utilized and gathered to provide data on algal pyrolysis kinetics. To predict the pyrolysis kinetics at a heating rate of 200°C/min, artificial neural networks (ANN) has been utilized. Results show that ANN predicted the outcome of pyrolysis kinetics which had a correlation with heating rates (10°C, 25°C, and 50°C) of the sample. This is quantified by the correlation coefficient during training which is 0.9972. The average fit quality of the derived model with respect to the experimental data is 98.51%. This work can be improved by considering other hyperparameters for the neural network. This work can also be extended to other compounds besides lablab biomass.