The present study aimed to analyze the literature on artificial intelligence development in higher education and to identify the inhibiting factors and enabling conditions within Iran’s higher education system. This applied study was conducted using a qualitative meta-synthesis approach. The research population included studies published between 2020 and 2025; following a systematic search, 100 studies were assessed and 23 articles were purposively selected. Data were analyzed through qualitative content analysis and systematic literature synthesis. During coding, 200 codes were extracted and organized into 46 concepts and 20 key categories. The credibility of the findings was confirmed through Cohen’s kappa. The inhibiting factors were classified into ten main categories: infrastructural and technical barriers, economic and financial constraints, lack of understanding of AI functions and benefits, absence of practical and pilot initiatives, limited stakeholder participation, cultural and behavioral barriers, educational and cognitive challenges, ethical and security concerns, issues of educational equity, and lack of policy-making and legal frameworks. The enabling conditions were grouped into ten categories: development of integrated technological infrastructure, formulation of national strategy and vision, positive organizational culture-building, transformation of educational processes and content, personalized learning and student performance enhancement, career foresight and economic development, increased faculty productivity, knowledge-based governance, integration into educational planning, and strategies for improving the quality of teaching and research. The findings can serve as a roadmap for policymakers and higher education administrators to develop realistic and responsible strategic plans.