Climate change is a complex global challenge with increasingly severe consequences for humanity as natural disasters multiply, sea levels rise, and ecosystems falter. Collective and urgent action is necessary to limit the extent of climate change impacts and adapt to their effects. Such action can take many forms, from designing smart electric grids (Nweye et al. [2023]) to tracking greenhouse gas emissions through satellite imagery (Bonczak et al. [2023]). Machine learning (ML) can be one useful tool for tackling climate change across multiple scales and sectors via mitigation and adaptation (Rolnick et al. [2022]). Success in both strategies requires encouragement of closer collaboration between diverse disciplines and stakeholders. This workshop intends to bring together those applying ML to climate change challenges and facilitate cross-pollination between ML researchers and experts in complementary climate-relevant fields. Building on our past workshops on this topic, we specifically focus on two aspects that fosters the maturity of ML applications for tackling climate change. The workshop will shed light on work that deploys, analyzes or critiques ML methods and their use for climate change adaptation and mitigation. In addition, we will discuss the key investment mechanics and policy frameworks needed for these applications to leapfrog towards just and balanced large-scale deployment and achieve positive societal-scale impact.