Abstract This work was motivated by the increasing need for proper metrics and tools to demonstrate the effect of mechanical performance, as a function of concrete mix composition, in dictating the dimensions of structural elements and associated costs and embodied carbon dioxide (CO2) emissions. Mixture compositions associated with different concrete technologies were compared using multi-criteria comparison indices derived using structural design considerations and calculated using information on compressive strength, volumetric embodied CO2 and unit costs. In addition, predicted compressive strengths obtained with machine learning (ML) models are used to calculate these indices for a domain of mix proportions associated with ultra-high-performance concrete materials to generate multi-objective density diagrams (MODDs). The makeup of this tool facilitates the evaluation of rather complicated trends associated with mix proportions and multi-objective outcomes, allowing ML-based tools to be of easy interpretation by industry personnel with no expertise in artificial intelligence. MODDs could be used as aids in the decision-making process during mix design stages and serve as proof of mixture optimization that could be introduced in environmental product declarations. Results show that, in contrast to conventional wisdom, high-binder content and ultra-high strength concrete technologies are not necessarily detrimental to cost and/or eco efficiencies. For the applications evaluated herein, optimum solutions were mostly obtained with these types of concrete, suggesting that industry trends toward requiring minimization of embodied carbon footprint on a per volume of concrete basis are misguided and should not be used as a standalone metric to minimize the total carbon footprint of concrete structures.