As data becomes more readily available at higher resolutions and with more covariate information, more advanced statistics is needed to address the complexity such data presents. This session highlights recent developments in statistical artificial intelligence and machine learning to analyze high dimensional, complex data as it pertains to the environment and health. In particular, measurements are often made in the environment, whether it is remotely sensed climate data or repeated measurements of exposures to potential contaminants, and interest lies in how these measures may correlate with health - whether it be human, plant or animal health. The speakers in this session come from diverse backgrounds but all present novel methods at the AI-environment-health nexus. Elif Acar is an Associate Professor at the University of Guelph and will provide overview talk with a focus on species distribution modelling. Ed Boone is a Professor of Statistics at Virginia Commonwealth University and has expertise in Bayesian methods for diverse applications, such as species movement patterns. Jia Wei is a PhD student in Statistics at the University of Guelph and is developing regularized regression or classification models when the set of predictors contain structural information according to a graphical Markov model. Aman Bhullar is a Postdoctoral Fellow at Agriculture and Agri-Food Canada and has exploited remotely sensed soil-climate-landscape data to build machine learning models for agricultural crops.