This chapter examines the negative aspects of artificial intelligence (AI) in the pursuit of environmental sustainability. Through better data analysis, forecasting, and efficient resource management, AI has great potential to advance sustainability. However, the creation, implementation, and operation of AI systems require significant computing power, leading to high energy consumption and a growing carbon footprint, especially when large-scale machine learning models and data centres are involved. Rapidly advancing AI-based environmental monitoring technologies often rely on resource-intensive hardware with short lifespans, contributing to the rising problem of electronic waste. AI-driven optimization in industries such as transportation and agriculture may unintentionally lead to overexploitation of natural resources. The opacity of many AI systems can hinder the detection of unsustainable practices, reducing transparency and accountability in environmental governance. Furthermore, the use of manipulated or insufficient environmental data to train AI models could result in poor decision-making and worsen ecological impact. Excessive reliance on AI could also place too much trust in technical solutions, diverting attention from systemic policy reforms and grassroots environmental initiatives, which are equally important. Incorporating AI into sustainability strategies requires a balanced approach that considers environmental costs. Responsible AI development must prioritize energy-efficient algorithms, circular design principles, and open, fair governance frameworks to mitigate future effects. It is imperative to acknowledge and address its downsides to ensure that AI becomes a true catalyst in the global effort for environmental sustainability.