Climate change is one of the most pressing challenges of the twenty-first century, demanding swift, innovative and scalable solutions to mitigate its impact and enhance societal resilience. Traditional computing methods are increasingly inadequate for processing the vast, dynamic and nonlinear data associated with environmental systems. The convergence of quantum computing, artificial intelligence (AI) and high-performance computing (HPC) presents a transformative opportunity to rethink climate adaptation strategies. This chapter examines the rising role of Quantum Artificial Intelligence (QAI) in fostering environmental resilience. Quantum computing, leveraging superposition and entanglement, enables complex computations at unprecedented speeds. When integrated with AI and machine learning, quantum systems can model climate dynamics, predict extreme weather, optimise resource use, and address environmental challenges. QAI holds immense promise for interpreting climate uncertainties and developing robust, evidence-based adaptation plans. The chapter explores QAI’s technical and theoretical foundations and its applications in environmental monitoring tracking biodiversity, analysing satellite land use data and assessing air and water quality in real time. It also highlights quantum-enhanced models in disaster and climate forecasting, where precision and speed are vital for safeguarding infrastructure and lives. Real-world use cases and pilot studies illustrate QAI’s growing impact in both developed and developing regions. Strategic recommendations for future research, policy development, and capacity building are offered, alongside a critical look at current limitations such as scalability, hardware access and the quantum skills gap. Ultimately, QAI is presented not as a distant innovation but as a present and evolving technology capable of reshaping humanity’s response to a rapidly changing climate. The chapter contributes to the discourse on quantum AI’s transformative role in environmental resilience, development and global climate governance, while acknowledging the critical challenges faced by integrating QAI into decision-making processes.