The safe operation of petroleum transportation pipelines is central to protecting human lives, ecosystems, and national energy infrastructure. Traditional monitoring frameworks, while effective in detecting anomalies such as pressure fluctuations or leaks, are often limited by delayed response times, manual intervention requirements, and the inability to adapt to dynamic risk environments. Recent advances in artificial intelligence (AI) present transformative opportunities for pipeline safety management by enabling real-time analytics, predictive modeling, and automated compliance verification. At a broader level, AI-enabled frameworks can integrate heterogeneous data streams from sensors, supervisory control systems, geospatial platforms, and environmental monitoring networks to construct a holistic operational picture. These frameworks support advanced anomaly detection, predictive maintenance, and early spill prevention through machine learning models that identify subtle precursors of failure. Narrowing the focus, the proposed study explores the development of an AI-enabled safety framework tailored for petroleum pipeline operations, emphasizing adaptive risk detection algorithms, natural language processing for regulatory text analysis, and digital twin integration for scenario testing. Additionally, the framework incorporates regulatory compliance monitoring by automating reporting, flagging deviations, and ensuring adherence to environmental and safety mandates. By aligning safety engineering principles with AI capabilities, the framework addresses both technical reliability and legal accountability. This approach not only reduces spill incidents and associated remediation costs but also strengthens stakeholder confidence in pipeline governance. Ultimately, embedding AI within safety frameworks represents a proactive and resilient pathway for petroleum transportation systems to meet evolving safety standards and environmental expectations.