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Description: To mitigate the effects of climate change on public health and conservation, we need to better understand the dynamic interplay between biological processes and environmental effects. The state-of-the-art, which has led to many important discoveries, utilizes numerical or statistical models for making predictions or performing in silico experimentation, but these techniques struggle to capture the nonlinear response of natural systems. Machine learning (ML) methods are better able to cope with nonlinearity and have been used successfully in biological applications (e.g., [1–3]), but several barriers still exist, including the opaque nature of the algorithm output and the absence of ML-ready data. Here, we propose to significantly advance technologies in ML and create a new interdisciplinary field, computational ecogenomics. We propose to do this by (a) designing ML techniques for encoding heterogeneous genomic and environmental data, and mapping them to multi-level phenotypic traits, (b) reducing the amount of necessary training data, and (c) developing interactive visualizations to better interpret ML models and their outputs. These advances will responsibly and transparently inform policy to maximize resources during this crucial window for planetary health, while revealing underlying biological mechanisms of response to stress and evolutionary pressure.