For decades, researchers have examined conflict processing in cognitive tasks, commonly referred to as conflict tasks. However, evidence showing diverging delta functions (Pratte et al., 2010) suggests that cognitive mechanisms differ across tasks. Unlike traditional approaches that rely solely on reaction times and error rates, computational models offer a deeper understanding by making precise mathematical assumptions about underlying cognitive processes. These models enable researchers to test and falsify competing explanations systematically. Particularly, the Diffusion Model for Conflict tasks (DMC; Ulrich et al., 2015) has proven to be an effective tool for explaining performance in conflict tasks, as it successfully predicts diverse delta function patterns across different conflict tasks (Hübner et al., 2019). However, the DMC’s application involves substantial computational effort and faces challenges in parameter recovery. In this study, we use BayesFlow (Radev et al., 2022) for Amortized Bayesian Inference to fit the DMC to behavioral data. This method provides an efficient alternative to traditional estimation techniques, as once a set of deep neural networks is trained, parameter estimation can be performed in real time. Through a simulation study, we assessed the impact of prior selection on parameter recovery, simulation-based calibration, and reliability. Additionally, we explored how stimulus spacing influences conflict-processing parameters in a flanker paradigm, confirming effects of spacing and congruency on both automatic and controlled drift rates. Our findings highlight the validity of the DMC and contribute to the existing literature by offering a novel and efficient method for parameter estimation within a Bayesian framework.