Climate change jeopardizes long-term global sustainability. Significant issues require innovative, persuasive solutions, such as utilizing cutting-edge technologies supported by robust scientific understanding. The integration of quantum machine learning (QML) with IoT devices presents promising solutions for mitigating climate change by improving climate modelling, forecasting, and monitoring. Classical methods cannot process the immense climate datasets from IoT sensors as efficiently as QML algorithms. QML results in more precise predictions and informed decision-making, using IoT devices to collect data. This synergy has the potential to expedite decarbonization, improve resource management, and strengthen the capacity to predict and mitigate climate-related hazards. Significant advancements have been made in quantum machine learning. Therefore, a framework that harnesses the capabilities of quantum computing to address multifaceted, multi-domain challenges such as environmental conservation and climate change is essential. The objective of this chapter is to focus on recently reported studies that employ quantum machine learning with IoT devices to address sustainability and climate change concerns. Quantum machine learning methods and procedures possess certain restrictions. These methods are summarized, along with the prediction of weather using meteorological data, climate monitoring, possible hazardous conditions, and the discussion of opportunities and challenges.