Machine learning based predictive models are being extensively applied for predicting combustion properties like derived cetane number (DCN), which is the measure of a fuels ignition quality. In the present work, a comprehensive model was developed using artificial neural networks (ANN) that can predict the DCN of fuels containing a large number of chemical classes like paraffins, iso-paraffins, olefins, naphthenes, aromatics, alcohols, ethers, aldehydes, ketones and esters. Experimental DCN’s of 275 fuels was used as a dataset and the composition of the fuels expressed in the form of twelve functional groups and two structural parameters namely, branching index (BI) and molecular weight were used as the input features for the model. A feed forward neural network with two hidden layers with 40 neurons in each layer was developed using Levenberg-Marquardt algorithm. The developed model was validated with 15% of the data points that were randomly generated and kept aside for validation. A regression coefficient (R2) of 0.99 was observed between the predicted and the experimental values along with an average absolute error of 1.1. The results showed that the developed model was successful in predicting the DCN of fuels and can be applied to pure compounds, blends and real fuels containing diverse chemical functionalities.