<p>Artificial Intelligence (AI) has emerged as a vital instrument in tackling intricate environmental issues, including remediation operations. This review examines contemporary research trends in applying AI technologies for environmental remediation, highlighting AI's contribution to improving efficiency, accuracy, and sustainability in managing polluted air, water, and soil. AI techniques, particularly machine learning and deep learning have been successfully employed to forecast pollution trends, enhance remediation approaches, and assess the ecological effects of contaminants. Research has shown AI's proficiency in real-time data processing, enabling swift responses to environmental hazards and enhancing decision-making procedures. Moreover, AI-driven models have created advanced remediation technologies, including intelligent bioremediation systems and nanotechnology-based solutions designed for specific pollutants and environmental circumstances. The use of AI in environmental remediation signifies substantial progress, providing scalable and economical solutions to worldwide pollution challenges. Nonetheless, issues about data quality, model openness, and the necessity for multidisciplinary collaboration are emphasized. Future studies must enhance AI approaches, broaden their usefulness, and guarantee ethical and sustainable utilization. This assessment highlights AI's capacity to transform environmental remediation initiatives, fostering a cleaner, safer, and more sustainable environment.</p>