This study proposes a computational framework for assessing operational compliance with the IATF 16949:2016 automotive quality management standard. A case study was conducted at the State Company for Automotive Industry and Equipment (Iraq), focusing on a car battery manufacturing facility. The framework integrates structured operational data with machine learning–assisted gap analysis to quantify deviations between actual practices and standard requirements. Key operational domains - including operational planning, control of externally provided processes, production and service provision, product release, and non-conformity management - were evaluated using weighted scoring models and statistical inference. Compliance levels were expressed through normalized conformity indices, enabling the identification of critical process deficiencies. The results indicate an overall compliance level of 55%, corresponding to a 45% operational gap relative to IATF 16949:2016 requirements. The findings demonstrate that data-driven compliance assessment enhances diagnostic accuracy and supports decision-making in quality management systems. The proposed approach contributes to the advancement of digital quality assurance, predictive compliance monitoring, and Industry 4.0-oriented manufacturing systems.