The on-going environmental changes challenge the ever-increasing use of digital technologies. Tools such as Green Algorithms or Carbontracker provide support for estimating the environmental impact of calculations (e.g., training a machine learning model). However, these tools only account for the dynamic consumption induced by calculations and only document carbon footprint while other types of impacts, such as resource depletion, are not evaluated. To provide a more comprehensive assessment of machine learning impact, we propose a modeling of graphics cards manufacturing impacts and a multi-criteria estimation tool called MLCA that accounts for the production impacts of hardware used to perform calculations. We evaluate MLCA through three reproduction studies thereby showing the validity of the assessments as well as the contribution of evalu- ating diverse impact categories over different life cycle phases. We hope this tool will help better understand the environmental impacts of Machine learning as a whole.