The advent of Artificial Intelligence (AI) has brought advancements in many domains, reshaping the way we live and work. The rapid expansion of AI technologies has raised concerns in terms of energy consumption, particularly regarding their environmental footprint. Thus, the urgency of developing AI systems that not only deliver high performance but also minimize their carbon footprint is growing. Recent studies only highlight AI's strides in enhancing model accuracy. However, these achievements have driven a surge in computational resource demands, limiting accessibility to the broader research community. As application domains and Machine Learning (ML) models have grown in complexity, the necessity of a vast volume of data and extended training durations has become consumption-critical.