Global climate change is projected to have a variety of local to regional scale impacts on human societies and ecosystems. The severity of these impacts (risk magnitude) depends upon the extent to which humans at the global scale mitigate Green House Gas (GHG) emissions through switching their fossil fuel-intensive behaviors to green behaviors, as well as adapting to adverse impacts of climatic change at local scales. While a flurry of studies and Integrated Assessment Models (IAMs) have independently investigated the impacts of switching mitigation behaviors or adaptation in response to different climate scenarios, little is understood about the feedback effect of how human risk perceptions of climate change could contribute to switching human behaviors from fossil-fuel intensive pathways to climate-friendly behaviors. Standard Global Climate Models assume a disconnect between risk/adaptation and mitigation behaviors. IAMs typically ignore human risk perceptions and focus on economic dynamics to predict the effect of adaption on mitigation behaviors (if at all). This study utilizes a suite of machine learning algorithms, both supervised and unsupervised, to explore how shifts in human risk perceptions, cognition, moral responsibility, social norms and activism can influence the adoption of green behaviors and induce support for mitigation to climate change at the policy level. The study uses mixed-pool “Climate Change in the American Mind” dataset collected between 2008 and 2014 (N=13,400) and apply three supervised (e.g., structural equation model) and three unsupervised (e.g. maximum spanning tree) machine learning algorithms on the data set to predict the adoption of green behaviors and policy action (e.g. see Figure 1). Further, the study replicates this analytical approach on a large sample of Eurobarometer dataset for 2000-2017 (N=150,605). Best fitting algorithms and models are identified through k-fold model validation approach by splitting the datasets in multiple folds of test and training samples. The implications of the best fitting algorithms for understanding the implicit processes by which people may translate climate risk perceptions into behavioral change and support for public policies aimed at mitigation to global climate change will be presented.