Graph theory is often applied to determine topological activity interactions of chemical substances by computing indices of topology. It has several uses in the development of in silico technologies. Machine learning, which has been extensively applied in numerous fields and is especially useful in the era of big data and artificial intelligence, can also be used to predict toxicity. However, topological indices need further consideration when used for predicting the toxicity of natural substances (flavonoids). The flavonoids are possessing 15 carbon atoms: C6 – C3 – C6 system. The basic molecular graphs of flavonoids are used to calculate various topological indices. In this paper, three types of metrics were developed per specified chemical components in natural products including the Statistical comparison amongst the topological index values (Wiener number, Polarity number Platt number) and the toxicity values of natural products was discussed.