The logistics sector, central to global trade, faces intensifying pressure to decarbonize while sustaining operational resilience. Responsible Artificial Intelligence (AI) offers a compelling framework to align technological innovation with eco-efficiency mandates. This paper investigates how AI applications such as real-time route optimization, energy-efficient warehouse automation, and predictive demand planning can reduce carbon emissions, fuel use, and resource waste by up to 30%. By integrating empirical models with emerging logistics practice, the study explores AI-driven adaptive systems and digital twins in fostering circularity and reducing systemic inefficiencies. It then critically examines ethical algorithm governance, lifecycle transparency, and the environmental footprint of AI infrastructures. To address these concerns, a governance framework is proposed, anchored in sustainability-aligned KPIs and accountability mechanisms. A conceptual model is developed to integrate Responsible AI principles across procurement, warehousing, transportation, and reverse logistics processes. By bridging AI’s operational benefits with norms of environmental stewardship and normative goals, this paper offers a roadmap for green transformation in logistics. The findings offer implications for industry stakeholders, regulators, and sustainability practitioners seeking to embed AI systems that are efficient, transparent, and ecologically conscious. This work contributes to interdisciplinary scholarship by harmonizing technological advancement with principles of responsible business practice and sustainable value creation