It is becoming more widely acknowledged that machine learning (ML) and artificial intelligence (AI) are revolutionary instruments in the battle against climate change. Using cross-sectoral applications, policy analysis, environmental monitoring, and predictive modelling, this review summarizes the most recent research on AI-enabled solutions for environmental sustainability. By analyzing enormous datasets from meteorological, geospatial, and oceanic sources, AI-driven predictive models improve climate forecasting, risk assessment, and mitigation planning, according to the findings. In environmental monitoring artificial intelligence (AI) combined with remote sensing and the Internet of Things (IoT) allows real-time tracking of biodiversity, air and water quality, and disaster risk, giving timely intelligence for intervention. AIs impact on governance is increased by Natural Language Processing (NLP), which analyses public opinion and climate policy documents to support evidence-based and socially responsive policymaking. Additionally, AI supports the Sustainable Development Goals (SDGs) by being used in disaster relief, sustainable agriculture, and energy optimization. Despite these developments, there are still many serious issues, such as interval in data quality, black-box nature of AI model, moral dilemmas and potential socio-economic injustice. The stress has been emphasized in all reviews the same access to standardized data protocols, persuadable AI framework, and technology resources. When it comes to including AI in climate strategies, interdisciplinary cooperation is an important architect that guarantees innovations, both socially and scientifically sound. In the time of unique environmental changes, Artificial Intelligence (AI) has the ability to promote global stability and flexibility, to become a major component of climate action by creating computational capabilities with environmental science and policy.