Modern football talent identification increasingly relies on emerging technologies, yet Iranian football academies continue to use primarily subjective coach assessments. This study developed a technology-based framework for football talent identification management in Iran. A two-phase quantitative approach was employed, utilizing the Delphi method for consensus building followed by classical DEMATEL for structural analysis. The expert panel comprised 15 male coaches and technical managers with minimum five years of experience in youth football talent identification, selected through purposive sampling. Twenty initial components were extracted from systematic literature review of international databases (Scopus, PubMed, Google Scholar) covering 2020-2025. Through three Delphi rounds, consensus was reached on ten final components measured on a four-point Likert scale, which were then analyzed using DEMATEL pairwise comparison matrices. Data analysis employed SPSS for Delphi consensus metrics and Excel for DEMATEL calculations including normalized matrices and total influence matrix. Results revealed a hierarchical causal system where biological maturation status emerged as the strongest causal factor with relationship index of 2.2744, followed by explosive power and perceptual-sensory coverage as driving factors. Speed splits demonstrated highest prominence (R+C=6.5774) as a central mediating variable, while passing accuracy, interpersonal interactions, and internal load indices functioned as effect factors. The framework provides evidence-based prioritization showing talent identification requires sequential assessment of biological foundations before technical-tactical abilities. This causal structure offers Iranian football academies a systematic approach to reduce subjective bias and prevent overlooking late-maturing talents during early selection stages.