Owing to rapid rise in global energy demands in various sectors including power, agriculture and automotive, there is a tremendous surge in fuel requirements to be met from conventional diesel fuel. Biodiesel fuels are emerging as a renewable alternative and offer better emission characteristics (except nitric oxides) compared to diesel fuel. In the present work, a composition based multi-linear regression approach is adopted to predict performance and emission characteristics of a single cylinder diesel engine run with four different biodiesel fuels. The biodiesel compositional effects on engine performance and emissions are captured through two new parameters, viz. straight chain saturation factor (SCSF) and modified degree of unsaturation (DUm) which can be estimated directly based on measured fatty acid methyl ester composition of biodiesel. The models developed are used to predict engine performance and exhaust emission characteristics at constant engine speed under varying load conditions from no load to full load. For each of the operating conditions, correlation matrix are calculated to examine the significance of fuel effects and it has been observed that the fuel effects get more dominant as full load conditions are approached. The predictions from the developed model are compared with experimental results and are found to be in good agreement with a regression coefficient of above 0.9 and average absolute deviations of less than 10% for all the investigated performance and emission parameters.