The astonishing outcomes produced by artificial intelligence applications offer researchers a new perspective and increasingly necessitate determining the role of AI in the traditionally human-mediated transmission of ḥadīth. Each discipline has its own methodological principles, and history shows that many contentious issues have been resolved through the specific methodologies of the respective fields. In this context, the integration of classical logic into Islamic jurisprudence by scholars such as al-Fārābī and al-Ghazālī opened the door for Aristotelian logic to find broader acceptance within Islamic sciences. However, a noticeable skepticism toward logic has existed in ḥadīth studies from its early period. Leading authorities like Ibn al-Ṣalāḥ, al-Nawawī, Ibn Taymiyya, and al-Suyūṭī expressed opposition to logic, indicating that classical logic was not integrated into ḥadīth methodology. Nevertheless, it is evident that logic, as a tool for attaining accurate knowledge, has potential utility. Lotfi Aliaskerzadeh (d. 2017) proposed a theory that, contrary to the definitiveness of binary logic, more than two sets—namely subsets and intersections—could exist, and argued that knowledge could be generated around uncertainty rather than certainty. It proposes that beyond two mutually exclusive sets, there may exist subsets and intersections, arguing that knowledge can be constructed around uncertainty rather than certainty. The core of the theory suggests that ambiguity may be more functional and outcome-oriented than definitiveness, with the principle of ‘minimal determinacy required for necessity’ adopted. Fuzzy logic emphasizes approximate values shaped by real-life variability. As a mathematical discipline, it transforms linguistic categories into quantifiable formulas. While classical logic operates on binary values between 0 and 1, fuzzy logic employs a continuum of values in this range. This constitutes the foundation of fuzzy set theory, which involves condition assessment, conceptual modeling, membership functions, rules, weight determination, and output calculation. At this stage, outputs are derived using fuzzy-logic formulas, such as the Mamdani method, and experimental applications can be carried out using software like MATLAB. When examining ḥadīth methodology, one can observe that judgments and conclusions are often based on approximations rather than absolute distinction-a natural consequence of human-constructed methodology. For example, ḥadīths are classified based on authenticity into ṣaḥīḥ (authentic), ḥasan (good), ḍaʿīf (weak), and mawḍūʿ (fabricated). Some ḥadīths are considered ṣaḥīḥ li-ghayrihi (authentic due to supporting evidence) despite certain deficiencies. The same applies to ḥasan ḥadīths. A weak ḥadīth may be elevated to ḥasan li-ghayrihi when corroborated. Hence, a ḥadīth considered rejected (mardūd) in one context may be accepted (maqbūl) in another. According to classical logic, a proposition cannot belong both to a set and its complement. However, fuzzy logic accommodates approximate values-e.g., a fully authentic ḥadīth may be rated as 1, a fabricated one as 0, and a weak ḥadīth as 0.1 or 0.2, depending on the degree of weakness. With supporting narrations, this value can rise to 0.5 or 0.6, qualifying it as ḥasan li-ghayrihi. This study is designed around the problem of why classical logic has not been integrated into ḥadīth methodology and explores the potential compatibility of fuzzy logic-now widely used in various fields-through modeling examples. As an introductory-level investigation, it asks whether the foundational concepts of ḥadīth methodology align with the systematic framework of fuzzy set theory. A data-driven analysis is presented on topics ranging from the definition of a Companion to the principles of criticism and validation (jarḥ and taʿdīl). A model for assessing ḥadīth authenticity is proposed, with empirical data used to discuss the applicability of fuzzy set theory in ḥadīth studies. A key finding is that ḥadīth experts must actively contribute to developing modeling frameworks and rules to minimize errors in AI applications and enhance fuzzy logic’s functionality, particularly in determining approximate values and grading processes.