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The US military operate in complex and harsh environments with regular risks of succumbing to heat- or cold-related injuries that could have both negative mission and individual health consequences. Objective and Methods: This effort collects human rest and exercise-based research data to compare and validate methods and mixed modeling approaches to provide a clear outline of predictive methods for determining physiological responses to hot and cold exposure (e.g., rise or fall in skin and core body temperatures) based on individual, environment, clothing, and activity. Data: This study collects human and non-human (clothing biophysics) data. Human research data used is from individuals during rest and exercise exposed to hot and cold environments (n = 51); while clothing data is a full range of clothing tested on sweating thermal manikins for measures of thermal and evaporative resistance (n = 93). From this combined data, the goal is test equations or methods for predicting general risk of heat- and cold- related injuries based on individual inputs. Two assessments are conducted, one to assess heat stress predictions (rise of core body temperature) and a second for assessing cold stress predictions (skin temperature fall). Conclusions: Analyses in the heat stress assessment showed empirical methods are capable of predicting within acceptable accuracy rise in core body temperature from group mean data; while individual-based predictions have been shown to be accurate to within an acceptable bias of ± 0.27°C for both in hot and humid environments laboratory (-0.10 ± 0.36) and field conditions (0.23 ± 0.32). Both rational and empirical methods were shown to acceptably predict skin temperatures to within the observed standard deviation (23.14 ± 9.35) (bias, -0.77 ± 3.69°C; MAE, 2.22 ± 3.05°C; and RMSE, 1.49 ± 3.05°C).