The undeniable benefits of Artificial Intelligence (AI), particularly Machine Learning (ML), have revolutionized the development of traditional software systems and given rise to ML-enabled systems. It is important to consider Green AI from a Software Engineering (SE) perspective when designing ML-enabled systems. This approach allows us to develop environmentally friendly ML ES that are both accurate and energy-efficient. One of the techniques employed to enhance the accuracy of ML-enabled systems is the use of Machine Learning Model Composition (ML MC). However, there is currently little empirical evidence to understand its concrete impact and how to effectively apply it within the context of Green AI. Therefore, this research aims to investigate the existing types of ML MC from the perspective of Green AI, providing guidance for practitioners in the design and implementation of ML-enabled systems utilizing these techniques.