In the present investigation, biodiesel (BD) production from Acacia concinna nonedible seed oil using physical pretreatment (turbulent agitation method) and transesterification process has been optimized and modeled using neural network Artificial neural network- Adaptive neuro-fuzzy inference system (ANNANFIS), Grey relational analysis and desirability function approach approach considering both quantity (production yield) and quality (fuel properties) responses. Different process parameters were examined for their relative significance on output responses. At optimized process variables, methanol/oil (8.3:1), catalyst KOH (0.95 wt%), and reaction temperature and time (65 degrees C and 37.5 min), augment the yield and calorific value by 17.2 and 5.77% and reduce the viscosity and free fatty acid value by 18.26 and 57.30%, respectively, with global desirability of D = 0.664. The produced BD was characterized by H-1 NMR, fatty esters (GC analysis), and fuel properties. The developed model equations for output responses help in accurate prediction of results. A. concinna feedstock proved to be a viable source for biodiesel production.