The adoption of digital technologies in Iran’s banking sector is a major challenge, as some banks and financial institutions face technical and infrastructural limitations that make the implementation of digital technologies difficult. The purpose of this dissertation is to design a model for digital technology adoption in the banking industry using an Industry 4.0 approach and the Adaptive Neuro-Fuzzy Inference System (ANFIS) modeling technique under various scenarios. Accordingly, the research methodology follows a mixed-methods approach (qualitative–quantitative). The research population and sample were purposefully selected and consisted of 35 experts with extensive experience and deep familiarity with concepts related to digital technology adoption in this industry. First, based on an in-depth review of the theoretical foundations and previous studies, the dimensions and components of digital technology adoption in the banking industry were identified through a fuzzy Delphi approach and expert opinions. Then, six different scenarios were designed and analyzed using the ANFIS method. The results of this study indicate that the adoption of digital technology in the banking industry is simultaneously influenced by technological factors, customer experience, infrastructure, innovation, human and cultural factors, regulations, and social responsibility. The proposed neuro-fuzzy model can be effectively used to predict and optimize the level of digital adoption.