Waste management is a major sustainability challenge for oil-producing nations, especially in arid regions with low environmental resilience. Kuwait’s oil sector generates massive amounts of produced water, drilling waste, sludge, contaminated soil, and emissions. This strains both local ecosystems and regulatory systems. Despite ongoing mitigation efforts, current practices remain reactive, fragmented, and lack advanced digital integration. This study explores how Artificial Intelligence (AI) can improve waste management sustainability in Kuwait’s oil industry. We synthesized extensive literature on waste management across Kuwait, the Gulf Cooperation Council (GCC), and major global oil producers. Our comparative analysis shows that while waste streams are similar, there are significant gaps in treatment technologies, regulatory enforcement, and digitalization. Based on these insights, we developed an AI-driven sustainability framework tailored to Kuwait’s operational and institutional context. The framework integrates digital sensing, AI analytics, regulatory alignment, and organizational readiness. It enables predictive monitoring, anomaly detection, optimized waste handling, and data-driven remediation. Ultimately, findings show that Kuwait’s main challenge is not the type of waste it produces, but its technological and institutional readiness. By adopting AI, Kuwait can shift from reactive waste control to proactive environmental governance, directly supporting Kuwait Vision 2035. Theoretically, this study bridges AI, sustainability, and oil-sector governance. Practically, it provides a scalable roadmap for AI adoption in resource-intensive industries.