The growing global demand for clean and sustainable energy has reignited interest in nuclear power as a carbon-free alternative to fossil fuels, driving an increase in uranium mining. However, uranium extraction releases radioactive elements along with toxic and heavy metals, posing serious environmental risks. A combined narrative and systematic review was employed to evaluate remediation mechanisms, performance trends, sustainability, and emerging technological advancements. The results indicate that phytoremediation remains the most extensively studied and field-applicable technique, while phycoremediation offers rapid uptake in aqueous systems and mycoremediation demonstrates higher tolerance to extreme conditions. However, limitations such as slow remediation rates, site-specific performance, and scalability challenges restrict their widespread implementation. This study also highlights the emerging role of artificial intelligence and machine learning in optimizing remediation processes, although their application remains limited, particularly in fungal systems. Furthermore, the integration of nature-based solutions into nuclear waste management frameworks, aligned with international safety standards, presents a promising pathway for sustainable remediation. Future research should focus on developing hybrid remediation strategies, establishing performance thresholds under high contamination conditions, and advancing AI-driven, site-specific optimization models to enhance efficiency and scalability.
Pradhananga et al. (Wed,) studied this question.