The widespread integration of Machine Learning (ML) in software systems has brought forth unprecedented advancements, yet the surge in energy consumption raises ecological concerns. This research addresses the environmental impact of ML development, focusing on the energy implications of design decisions in ML-based systems. This thesis aims to offer insights into the energy consumption patterns influenced by decisions such as deployment and training location. Different case studies on ML-based systems will be conducted to validate and demonstrate the implications of these design choices. The expected outcomes encompass actionable insights, validated through rigorous evaluations, and the development of a decision support tool for ML-based system development, with energy efficiency at its core. This work contributes to the broader field of Green AI by addressing a critical gap and guiding the transition towards a more sustainable AI landscape.