The paper examines the paradigm shift in responsible governance, corporate social responsibility (CSR), and the looming emergence of environed issues in the economic landscape and form of organization in the automotive industry. To amalgamate ESG compliance-oriented on reducing and carbon footprint, ethical labor and algorithmic transparency with AI auditing efficiency-oriented on identifying fraud, cost optimization, and alignment governance and sustainable financial performance indicators supply chain resilience, reduced emissions and social impact, we recommend an inclusive meta-framework. We examine three central relationships through Structural Equation Modeling (SmartPLS) on data of 60 firms and 120 implementation cases on the automotive industry where sustainable outcomes are greatly improved by AI auditing (path coefficient 0.72, p < 0.001), ESG governance mediates 35% of financial stability, and logistics risk management is involved in improving operational stability at a high price of energy consumption and AI precision. We discover that AI-aided ESG technology is 40–58 more efficient than the traditional method of compliance checks, but introduces new challenges, such as the bias of an algorithm when it comes to sourcing audits and energy usage intensity. The report gives actionable guidelines on how auto makers can endeavor to focus on explainable AI to remain competitive with regulation and to adopt new form of governance that is automated and ethically accountable as well as maximize quantifiable sustainability KPIs including Scope 3 emissions monitoring. The study offers a measurable roadmap to the next-generation of car ESG systems where business profitability is matched with the planetary sustainability limits because it resolves the connection between the computational power and the socio-ethical accountability.