Over the past few decades, greenhouse gases (GHGs) have been broadly studied to determine their impact on the earth’s changing climate. Despite the universal significance of understanding all sources of GHG emissions, the general focus in the scientific community has been on anthropogenic producers of emissions such as agriculture, landfill, industry, etc. This has left a knowledge gap surrounding emissions from natural sources of GHGs. Natural sources make up between 40-50% of total global methane emissions with wetlands constituting upwards of 40% of those natural sources. Yet, due to the high cost of existing flux measurement systems, the predicted emission rates in an individual wetland are poor. Given that methane (CH4) emissions have a global warming potential (GWP) over 25 times that of carbon dioxide (CO2), quantifying environmental drivers and flux rates is crucial for filling the knowledge gap. We are addressing this need by developing a low-cost CH4 sensor that can autonomously collect data in the field on battery power. Although technology to collect these data exists, one critical issue is that it can be a painstaking process to convert raw signals into meaningful data using a low-cost sensor for which a standardized calibration approach has not been developed. For example, CH4 data can be collected in the environment using a low-cost metal oxide sensor, but conversion of raw signals to CH4 concentrations requires an extensive calibration process for each individual sensor. We are evaluating machine learning approaches to create calibration protocols and equations that will reduce the time and expertise required to calibrate an individual sensor. We present the work of this modeling approach and present a case study using the system we have developed to measure GHG fluxes from depressional wetlands in the Midwestern USA.