This chapter explores the transformative potential of artificial intelligence (AI) and machine learning (ML) in enhancing marine pollution detection and management within West Africa’s coastal regions. These regions, which are vital for their rich biodiversity and economies reliant on fisheries and tourism, face escalating environmental challenges such as marine pollution, coastal degradation, and climate change. However, effective pollution management is constrained by inadequate infrastructure, limited monitoring systems, and insufficient financial resources. By conducting a rapid review of peer-reviewed studies published between 2015 and 2024, this chapter examines how AI-driven technologies—particularly ML algorithms and remote sensing—can address these challenges. The findings reveal that AI applications improve pollution detection by monitoring sources such as industrial discharges, plastics, and oil spills, enabling real-time data analysis and timely responses to mitigate environmental impacts. Case studies from key areas, including Lagos (Nigeria) and other West African coastal stretches, highlight the practical applications of AI in strengthening resilience, enhancing biodiversity protection, and supporting sustainable resource management. Despite challenges such as limited infrastructure and funding, AI-driven systems show significant promise in advancing pollution control, improving environmental governance, and offering actionable insights for policymakers and stakeholders. This work contributes to the growing body of knowledge on how innovative technologies can transform pollution management and promote the sustainability of coastal ecosystems in West Africa.