The African oil and gas sector operates within a complex nexus of economic development, energy demand, and acute environmental sensitivity. Traditional environmental management approaches, often reactive and siloed, are increasingly inadequate for addressing the cumulative and interconnected risks of extraction activities. This paper proposes a paradigm shift towards proactive environmental stewardship through the integration of Big Data analytics. We explore the potential of diverse data streams—including real-time satellite imagery, IoT sensor networks from infrastructure, seismic data, social media sentiment, and historical environmental records—to build predictive models for environmental impact. The study outlines a framework for leveraging machine learning and AI to forecast potential incidents such as oil spills, gas flaring effects, groundwater contamination, and ecosystem disruption before they escalate. A key finding is that this data-driven foresight enables more effective resource allocation for mitigation, strengthens regulatory compliance, and fosters sustainable co-existence between energy projects and local communities. By harnessing Big Data, the African oil and gas industry can not only minimize its ecological footprint but also build greater operational resilience and secure its social license to operate, thereby revolutionizing its path toward sustainable development.