Utilization of apricot seed (AS) in lignite coal (LC)-based (co-)combustion process was aimed in the present study considering the apricot production capacity of Turkey. By this way, an alternative and also ecofriendly way was suggested for coal-based energy production plants located in Turkey. This purpose was tested by thermogravimetric analyses to demonstrate the advantageous sides of AS in reduction of ash amount and also environmental aspects based on harmful gases. The other important contributors of present study was the comparison of both statistical modeling and numeric optimization techniques for maximization of mass loss percentage (MLP, %) in response to (co-) combustion process. For this purpose, multiple non-linear regression (MNLR) and artificial neural network (ANN) models as data-driven modeling techniques, and response surface methodology (RSM) and particle swarm optimization (PSO) as numeric optimization approaches were utilized. Results demonstrated the accuracy of ANN and PSO in prediction of MLP (%) and optimization of operating conditions of (co-)combustion of AS and LC, respectively. Finally, Bayesian approach was applied to the best-fit MNLR model to identify the uncertainties in predictors of proposed model. Bayesian was found quite effective in identification of uncertainties that were not possible to be captured through deterministic ways.