Industry 4.0 has revolutionised manufacturing, presenting significant challenges for adoption, particularly in developing countries like India. This study identifies and evaluates challenges specific to the Indian automobile industry's implementation of Industry 4.0 to address this. Leveraging Latent Dirichlet Allocation (LDA), a machine learning-based text analysis algorithm, we discerned challenges from existing literature. Subsequently, employing the Delphi method, we refined these challenges, leading to a questionnaire-based survey and fuzzy Decision-Making Trial and Evaluation Laboratory (f-DEMATEL) data analysis to prioritise them. Our research framework involved collaboration with original equipment manufacturers (OEMs), suppliers, and academic experts who ranked 20 challenges by influence. Findings reveal divergent perspectives: OEM experts highlight concerns regarding outdated infrastructure, high initial costs, financial uncertainty, and a lack of strategy and standards. Supplier industries emphasise the importance of Information Technology and Research & Development departments, the maturity of Industry 4.0 tools, industry-academia collaboration, and addressing strategy and standards gaps. Academia underscores the need for financial support, government assistance, and organisational adjustments. These insights offer crucial guidance for managing Industry 4.0 challenges in the Indian automobile industry, facilitating targeted and practical implementation strategies.