Abstract(#br)The present work investigates the influence of advanced injection strategy on a common rail direct injection assisted diesel engine characteristics fuelled with biodiesel and conventional diesel. Also, an artificial neural network is employed to forecast engine characteristics. Engine test is conducted under 100% load condition through an optimized nozzle opening pressure of 500 bar. Pre-injection timing is fixed permanently at 30 °CA bTDC, main injection timing varied from 15 °CA to 21 °CA bTDC and post-injection varied from 6 °CA bTDC to 6 °CA aTDC sequentially. However, the pre, main and post-injection quantities are changed respectively from 5% to 15%, 70% to 90%, and 5% to 15%. Minimum carbon monoxide, unburned hydrocarbon and smoke emission of 0.01% vol., 8 ppm and 1.59... FSN are achieved with pre-injection timing of 30 °CA, main injection timing of 21 °CA bTDC, and post injection timing of 6 °CA bTDC for B100-15%-70%-15%. Maximum brake thermal efficiency and nitric oxide emission of 34.3% and 1114 ppm are achieved in B100-5%-90%-5% at advanced injection timing and higher nozzle opening pressure. Artificial neural network models conform to experimental results having a lower root mean square error and correlation coefficient values in a range of 0.01 to 0.02 and 0.980 to 0.998 respectively. An artificial neural network is mostly preferred over other theoretical and empirical models to higher accuracy in predicting the output. Hence, multiple injection strategy fuelled biodiesel significantly decreased the emission and improved the performance compared to mechanical, single and split injection strategy.