This study conducts a needs assessment to evaluate the critical gaps in industrial pollution monitoring and response systems in West Africa, with a focus on how artificial intelligence (AI) can address these deficiencies. Traditional methods such as manual sampling and laboratory analysis are constrained by infrastructure limitations, delayed response times, and low spatial coverage. In contrast, AI-driven technologies—including Internet of Things (IoT) sensors, machine learning algorithms, and autonomous drones—offer real-time monitoring, predictive analytics, and improved responsiveness. By synthesizing literature from 2015 to 2024 through a rapid review methodology, this study identifies key infrastructural, technical, and policy-related barriers hindering AI adoption in the region. It highlights the potential of integrating satellite-derived data, such as aerosol optical depth (AOD), to support continuous air quality tracking. The findings provide actionable strategies for deploying AI-enhanced systems to strengthen industrial pollution governance and support environmental sustainability. This review aims to provide valuable insights for policymakers and industry stakeholders, aligning with Sustainable Development Goals 12 (Responsible Consumption and Production) and 13 (Climate Action).