Environmental challenges, such as climate change, energy inefficiency, emissions, and waste, pose significant threats to global sustainability. These issues are critical due to their impact on ecosystems, economies, and human health, necessitating innovative solutions. Despite extensive research, there is a gap in integrating system dynamics (SD) and machine learning/artificial intelligence (ML/AI) for holistic environmental management. This study addresses this gap by reviewing literature from 2014–2024 that combines SD’s feedback-driven modeling with ML/AI’s predictive and optimization capabilities. The findings reveal that this hybrid approach enhances long-term planning and decision-making in areas like waste, water, and energy management, though its application remains limited, highlighting opportunities for broader adoption.