Abstarct: With the rapid advancement of emerging technologies, the utilization of artificial intelligence (AI) in human resource processes, particularly in recruitment, has become a strategic priority for organizations. However, the adoption of AI-baxsed recruitment in the Iranian banking industry faces fundamental challenges, the identification and analysis of which require a structured and data-driven approach. This study aims to identify and weigh the factors influencing the adoption of smart recruitment in Iranian banks. In the first stage, through a systematic literature review and the application of the Type-2 Neutrosophic Fuzzy Delphi method with experts, the key factors were extracted and clustered within the Technology-Organization-Environment (TOE) frxamework. Subsequently, the CRITIC quantitative method was employed to determine the relative importance of each sub-criterion. In the final stage, using the CODAS multi-criteria decision-making method, ten Iranian banks (five private and five state-owned) were evaluated and ranked across the three dimensions as well as overall. The findings reveal that 20 sub-criteria affect the adoption of smart recruitment in Iranian banks. At the criteria level, the organizational dimension ranked first with a weight of 0.452, followed by the technological dimension (0.29) and the environmental dimension (0.258). At the sub-criteria level, competitive pressure (0.0564), perceived ease of use (0.0543), top management support (0.05043), job loss (0.05043), and resistance to change (0.0532) were ranked as the top five factors. On average, private banks outperformed state-owned banks in technological and organizational dimensions. In contrast, state-owned banks achieved better results in the environmental dimension, particularly in areas such as interaction with regulatory institutions and benefiting from institutional support. Furthermore, through comparative analyses, rank trends, the identification of strengths and weaknesses, and cluster analysis of the banks, precise practical recommendations were provided to enhance the pathway toward adopting smart recruitment in each bank. By offering a localized operational model, this study can serve as a decision-making guide for bank managers, technology policymakers, and human resource professionals in advancing digital transformation and employing AI in recruitment processes.